├── .gitmodules ├── Applied-Machine-Learning-2019.md ├── Applied-Machine-Learning-2020-Spring.md ├── Applied-Machine-Learning-2020.md ├── Data-Handling └── README.md ├── Images ├── DrTaheri.jpeg ├── HH.jpg ├── Hands-On-ML.jpg ├── K.jpg ├── MLWP.jpg ├── MachineLearningMastery.png ├── Mafakheri.jpg ├── MrFaridi.jpg ├── MrHojatneia.jpeg ├── MrYeganeh.jpg ├── MsAmanian.jpeg ├── MsHosseini.jpeg ├── R.jpg ├── README.md ├── Y.jpg ├── bishop-1x.jpg ├── data-fallacies-to-avoid.jpg ├── homl-cover.jpg ├── james-1x.png └── shalev-shwartz-1x.jpg ├── NoteBooks ├── AdaBoost.ipynb ├── Data │ ├── README.md │ ├── Titanic.csv │ └── iris.csv ├── Decision-Trees.ipynb ├── README.md ├── Stratified-K-Folds-Cross-Validator.ipynb └── k-Nearest-Neighbors.ipynb ├── Projects ├── Projects-Fall-2021 │ ├── Data │ │ ├── Global-Features │ │ │ ├── Readme.md │ │ │ ├── bace_global_cdf_rdkit.zip │ │ │ ├── bbbp_global_cdf_rdkit.zip │ │ │ ├── clintox_global_cdf_rdkit.zip │ │ │ ├── delaney(esol)_global_cdf_rdkit.zip │ │ │ ├── lipo_global_cdf_rdkit.zip │ │ │ ├── qm7_global_cdf_rdkit.zip │ │ │ ├── qm8_global_cdf_rdkit.zip │ │ │ ├── sampl(freesolv)_global_cdf_rdkit.zip │ │ │ ├── sider_global_cdf_rdkit.zip │ │ │ ├── tox21_global_cdf_rdkit.zip │ │ │ └── toxcast_global_cdf_rdkit.zip │ │ ├── Readme.md │ │ ├── bace.csv │ │ ├── bbbp.csv │ │ ├── clintox.csv │ │ ├── delaney.csv │ │ ├── freesolv.csv │ │ ├── hiv.csv │ │ ├── lipo.csv │ │ ├── muv.csv │ │ ├── pdbbind_core.csv │ │ ├── pdbbind_full.csv │ │ ├── pdbbind_refined.csv │ │ ├── qm7.csv │ │ ├── qm8.csv │ │ ├── qm9.csv │ │ ├── sider.csv │ │ ├── tox21.csv │ │ └── toxcast.csv │ └── Readme.md └── README.md ├── Readme.md ├── Recitation-Assignments ├── Assignment-Set-1_Sample.ipynb ├── Assignments_Data │ ├── Assignment_Set_13_Data.csv │ └── Assignment_Set_8_Data.csv ├── README2020.md └── Readme.md ├── Tutorials └── README.md ├── _config.yml └── _layouts └── default.html /.gitmodules: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hhaji/Applied-Machine-Learning/dab9c127e2a9f05b964f194b3d1264f68c3029ec/.gitmodules -------------------------------------------------------------------------------- /Applied-Machine-Learning-2019.md: -------------------------------------------------------------------------------- 1 | Lecturer: [Hossein Hajiabolhassan](http://facultymembers.sbu.ac.ir/hhaji/)
2 | The Webpage of the Course: [Applied Machine Learning 2019](https://hhaji.github.io/Applied-Machine-Learning/)
3 | [Data Science Center](http://ds.sbu.ac.ir), [Shahid Beheshti University](http://www.sbu.ac.ir/)
4 | 5 | --- 6 | 7 | ### **Index:** 8 | - [Course Overview](#Course-Overview) 9 | - [TextBooks](#TextBooks) 10 | - [Slides and Papers](#Slides-and-Papers) 11 | 1. Lecture 1: [Toolkit Lab (Part 1)](#Part-1) 12 | 2. Lecture 2: [Introduction](#Introduction) 13 | 3. Lecture 3: [Empirical Risk Minimization](#Empirical-Risk-Minimization) 14 | 4. Lecture 4: [PAC Learning](#PAC-Learning) 15 | 5. Lecture 5: [The Bias-Complexity Tradeoff](#The-Bias-Complexity-Tradeoff) 16 | 6. Lecture 6: [The VC-Dimension](#The-VC-Dimension) 17 | 7. Lecture 7: [Toolkit Lab (Part 2)](#Part-2) 18 | 8. Lecture 8: [Linear Predictors](#Linear-Predictors) 19 | 9. Lecture 9: [Decision Trees](#Decision-Trees) 20 | 10. Lecture 10: [Nearest Neighbor](#Nearest-Neighbor) 21 | 11. Lecture 11: [Ensemble Methods](#Ensemble-Methods) 22 | 12. Lecture 12: [Model Selection and Validation](#Model-Selection-and-Validation) 23 | 13. Lecture 13: [Neural Networks](#Neural-Networks) 24 | 14. Lecture 14: [Convex Learning Problems](#CLP) 25 | 15. Lecture 15: [Regularization and Stability](#Regularization-and-Stability) 26 | 16. Lecture 16: [Support Vector Machines](#Support-Vector-Machines) 27 | 28 | - [Additional NoteBooks and Slides](#Additional-NoteBooks-and-Slides) 29 | - [Class Time and Location](#Class-Time-and-Location) 30 | - [Projects](#Projects) 31 | - [Practical Guide](#Practical-Guide) 32 | - [Fascinating Guide to Use Python Libraries (Machine Learning)](#Fascinating-Guide-For-Machine-Learning) 33 | - [Google Colab](#Google-Colab) 34 | - [Latex](#Latex) 35 | - [Useful NoteBooks](#Useful-NoteBooks) 36 | - [Grading](#Grading) 37 | - [Prerequisites](#Prerequisites) 38 | - [Linear Algebra](#Linear-Algebra) 39 | - [Probability and Statistics](#Probability-and-Statistics) 40 | - [Discrete Mathematics](#Discrete-Mathematics) 41 | - [Topics](#Topics) 42 | - [Account](#Account) 43 | - [Academic Honor Code](#Academic-Honor-Code) 44 | - [Questions](#Questions) 45 | - Miscellaneous 46 | - [Tutorials](https://hhaji.github.io/Applied-Machine-Learning/Tutorials) 47 | - [Data Handling](https://hhaji.github.io/Applied-Machine-Learning/Data-Handling) 48 | - [Projects](https://hhaji.github.io/Applied-Machine-Learning/Projects) 49 | 50 | --- 51 | 52 | ## Course Overview: 53 | ```javascript 54 | Machine learning is an area of artificial intelligence that provides systems the ability to 55 | automatically learn. Machine learning allows machines to handle new situations via analysis, 56 | self-training, observation and experience. The wonderful success of machine learning has made 57 | it the default method of choice for artificial intelligence experts. In this course, we review 58 | the fundamentals and algorithms of machine learning. 59 | ``` 60 | ## TextBooks: 61 | ![Book 1](/Images/shalev-shwartz-1x.jpg) ![Book 2](/Images/james-1x.png) ![Book 3](/Images/MachineLearningMastery.png) ![Book 4](/Images/MLWP.jpg) ![Book 5](/Images/bishop-1x.jpg) 62 | 63 | ``` 64 | Main TextBooks: 65 | ``` 66 | * [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning), by Shai Shalev-Shwartz and Shai Ben-David
67 | * [An Introduction to Statistical Learning: with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
68 | 69 | ``` 70 | Additional TextBooks: 71 | ``` 72 | * [Machine Learning Mastery With Python](https://machinelearningmastery.com/machine-learning-with-python/) by Jason Brownlee
73 | - [Python Codes](https://github.com/rupskygill/ML-mastery/tree/master/ml_with_python_code)
74 | * [Introduction to Machine Learning with Python: A Guide for Data Scientists](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413) by Andreas Mueller and Sarah Guido 75 | - [Notebooks](https://github.com/amueller/introduction_to_ml_with_python)
76 | * [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/people/cmbishop/?from=https%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fum%2Fpeople%2Fcmbishop%2FPRML%2F#!prml-book) by Christopher Bishop
77 | 78 | ## Slides and Papers: 79 | 80 | Recommended Slides & Papers: 81 | 1. ### Toolkit Lab (Part 1) 82 | 83 | ``` 84 | Required Reading: 85 | ``` 86 | 87 | - [Python Libraries for Data Science](https://github.com/hhaji/Tools-in-Data-Science#Python-Libraries-for-Data-Science)
88 | - **Exercises:** Practice Numpy in [LabEx](https://labex.io/courses/100-numpy-exercises)
89 | - **Exercises:** Practice Pandas in [LabEx](https://labex.io/courses/100-pandas-exercises)
90 | - **Exercises:** Practice Matplotlib in [LabEx](https://labex.io/courses/draw-2d-and-3d-graphics-by-matplotlib)
91 | 92 | ``` 93 | Additional Reading: 94 | ``` 95 | 96 | - [Tools in Data Science](https://hhaji.github.io/Tools-in-Data-Science/) 97 | - [R Tutorial for Beginners: Learning R Programming](https://www.guru99.com/r-tutorial.html) 98 | 99 | 2. ### Introduction 100 | 101 | ``` 102 | Required Reading: 103 | ``` 104 | 105 | - Slide: [Machine Learning: Types of Machine Learning I](http://www.lsi.upc.es/~bejar/apren/docum/trans/00-introAprendizaje-eng.pdf) by Javier Bejar 106 | - Slide: [Machine Learning: Types of Machine Learning II](http://www.lsi.upc.edu/~bejar/apren/docum/trans/01-apind-eng.pdf) by Javier Bejar 107 | 108 | 3. ### Empirical Risk Minimization 109 | 110 | ``` 111 | Required Reading: 112 | ``` 113 | 114 | - A Formal Model – The Statistical Learning Framework & Empirical Risk Minimization
115 | Chapter 2 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 116 | - **Exercises:** 2.1, 2.2, and 2.3 117 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
118 | - Slide: [Lecture 1](http://www.cs.huji.ac.il/~shais/Lectures2014/lecture1.pdf) by Shai Shalev-Shwartz
119 | 120 | 4. ### PAC Learning 121 | 122 | ``` 123 | Required Reading: 124 | ``` 125 | 126 | - Chapter 3 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
127 | - **Exercises:** 3.2, 3.3, 3.4, 3.5, 3.6, 3.7 128 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
129 | - Slide: [Lecture 2](http://www.cs.huji.ac.il/~shais/Lectures2014/lecture2.pdf) by Shai Shalev-Shwartz 130 | 5. ### The Bias-Complexity Tradeoff 131 | 132 | ``` 133 | Required Reading: 134 | ``` 135 | 136 | - Chapter 5 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
137 | - **Exercise:** 5.2 138 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
139 | - Slide: [Lecture 3](http://www.cs.huji.ac.il/~shais/Lectures2014/lecture3.pdf) by Shai Shalev-Shwartz
140 | - Paper: [The Bias-Variance Dilemma](https://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents/tutorials/bias.pdf) by Raul Rojas
141 | 142 | ``` 143 | Additional Reading: 144 | ``` 145 | 146 | - NoteBook: [Exploring the Bias-Variance Tradeoff](https://github.com/justmarkham/DAT8/blob/master/notebooks/08_bias_variance.ipynb) by Kevin Markham
147 | 148 | 6. ### The VC-Dimension 149 | 150 | ``` 151 | Required Reading: 152 | ``` 153 | 154 | - Chapter 6 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
155 | - **Exercises:** 6.2, 6.4, 6.6, 6.9, 6.10, and 6.11
156 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
157 | 158 | 7. ### Toolkit Lab (Part 2) 159 | 160 | ``` 161 | Required Reading: 162 | ``` 163 | 164 | - [Machine Learning Mastery With Python](https://machinelearningmastery.com/machine-learning-with-python/) by Jason Brownlee
165 | - [Python Codes](https://github.com/rupskygill/ML-mastery/tree/master/ml_with_python_code)
166 | - Data Exploration: 167 | - NoteBook: [Titanic 1 – Data Exploration](http://stamfordresearch.com/titanic-1-data-exploration/) by John Stamford 168 | - NoteBook: [Kaggle Titanic Supervised Learning Tutorial](https://www.kaggle.com/sashr07/kaggle-titanic-tutorial) 169 | - NoteBook: [An Example Machine Learning Notebook](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) by Randal S. Olson
170 | - Homework: [Take the 7-Day Machine Learning Challenge of Kaggle:](https://www.kaggle.com/page/machine-learning-course-ads?utm_medium=paid) Machine learning is the hottest field in data science, and this track will get you started quickly.
171 | 172 | 8. ### Linear Predictors 173 | 174 | ``` 175 | Required Reading: 176 | ``` 177 | 178 | - Chapter 9 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
179 | - **Exercises:** 9.1, 9.3, 9.4, and 9.6
180 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
181 | - Slide: [Tutorial 3: Consistent linear predictors and Linear regression](https://webcourse.cs.technion.ac.il/236756/Spring2014/ho/WCFiles/Tutorial%203.pdf) by Nir Ailon
182 | - NoteBook: [Perceptron in Scikit](https://github.com/chrisalbon/notes/blob/master/content/machine_learning/basics/perceptron_in_scikit-learn.ipynb) by Chris Albon
183 | - Paper: [Perceptron for Imbalanced Classes and Multiclass Classification](https://www.cs.utah.edu/~piyush/teaching/imbalanced_multiclass_perceptron.pdf) by Piyush Rai
184 | 185 | ``` 186 | Additional Reading: 187 | ``` 188 | 189 | - NoteBook: [Linear Regression](http://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb) by Kevin Markham
190 | - Paper: [Matrix Differentiation](https://atmos.washington.edu/~dennis/MatrixCalculus.pdf) by Randal J. Barnes
191 | - Lecture: [Logistic Regression](https://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch12.pdf) by Cosma Shalizi
192 | - NoteBook: [Logistic Regression-Analysis](https://nbviewer.jupyter.org/github/nborwankar/LearnDataScience/blob/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb) by Nitin Borwankar
193 | - [DataSets](https://github.com/nborwankar/LearnDataScience/tree/master/datasets)
194 | - NoteBook: [Logistic Regression](https://github.com/justmarkham/DAT8/blob/master/notebooks/12_logistic_regression.ipynb) by Kevin Markham
195 | - Infographic and Code: [Simple Linear Regression (100 Days Of ML Code)](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day2_Simple_Linear_Regression.md) by Avik Jain
196 | - Infographic and Code: [Multiple Linear Regression (100 Days Of ML Code)](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day3_Multiple_Linear_Regression.md) by Avik Jain
197 | - Infographic and Code: [Logistic Regression (100 Days Of ML Code)](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%206%20Logistic%20Regression.md) by Avik Jain
198 | 199 | ``` 200 | R (Programming Language): 201 | ``` 202 | 203 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
204 | * Blog: [Linear Regression](http://uc-r.github.io/linear_regression) by UC Business Analytics R Programming Guide
205 | * Blog: [Linear Regression with lm()](https://bookdown.org/ndphillips/YaRrr/linear-regression-with-lm.html) by Nathaniel D. Phillips
206 | * Blog: [Logistic Regression](http://uc-r.github.io/logistic_regression) by UC Business Analytics R Programming Guide
207 | 208 | 9. ### Decision Trees 209 | 210 | ``` 211 | Required Reading: 212 | ``` 213 | 214 | - Chapter 18 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
215 | - **Exercise:** 18.2
216 | - Slide: [Decision Trees](https://www.utdallas.edu/~nrr150130/cs6375/2015fa/lects/Lecture_6_Decision.pdf) by Nicholas Ruozzi
217 | - Slide: [Representation of Boolean Functions](http://freeusermanuals.com/backend/web/manuals/1522928344lec2.pdf) by Troels Bjerre Sørensen
218 | - Slide: [Overfitting in Decision Trees](https://www3.nd.edu/~rjohns15/cse40647.sp14/www/content/lectures/24%20-%20Decision%20Trees%203.pdf) by Reid Johnson
219 | - NoteBook: [Decision Trees](https://github.com/hhaji/Applied-Machine-Learning/blob/master/NoteBooks/Decision-Trees.ipynb)
220 | 221 | ``` 222 | Additional Reading: 223 | ``` 224 | 225 | - Paper: [Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?](http://jmlr.csail.mit.edu/papers/volume15/delgado14a/delgado14a.pdf) by Manuel Fernandez-Delgado, Eva Cernadas, Senen Barro, and Dinani Amorim
226 | - Blog: [Random Forest Classifier Example](https://chrisalbon.com/machine_learning/trees_and_forests/random_forest_classifier_example/) by Chris Albon. This tutorial is based on Yhat’s 2013 tutorial on [Random Forests in Python](http://blog.yhat.com/posts/random-forests-in-python.html). 227 | - [NoteBook](https://github.com/chrisalbon/notes/blob/master/docs/machine_learning/trees_and_forests/random_forest_classifier_example.ipynb)
228 | - NoteBook: [Titanic Competition with Random Forest](https://github.com/chrisalbon/kaggle/blob/master/titanic/titanic_competition_with_random_forest.ipynb) by Chris Albon
229 | - Infographic and Code: [Decision Trees (100 Days Of ML Code)]( https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%2025%20Decision%20Tree.md) by Avik Jain
230 | 231 | ``` 232 | R (Programming Language): 233 | ``` 234 | 235 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
236 | * Blog: [Decision Tree Classifier Implementation in R](http://dataaspirant.com/2017/02/03/decision-tree-classifier-implementation-in-r/) by Rahul Saxena
237 | * Blog: [Regression Trees](http://uc-r.github.io) by UC Business Analytics R Programming Guide
238 | 239 | 10. ### Nearest Neighbor 240 | 241 | ``` 242 | Required Reading: 243 | ``` 244 | 245 | - Chapter 19 (Section 1) of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
246 | - Slide: [Nearest Neighbor Classification](http://svivek.com/teaching/machine-learning/fall2018/slides/nearest-neighbors/nearest-neighbors.pdf) by Vivek Srikumar
247 | - NoteBook: [k-Nearest Neighbors](https://github.com/hhaji/Applied-Machine-Learning/blob/master/NoteBooks/k-Nearest-Neighbors.ipynb)
248 | 249 | ``` 250 | Additional Reading: 251 | ``` 252 | 253 | - NoteBook: [Training a Machine Learning Model with Scikit-Learn](https://github.com/justmarkham/scikit-learn-videos/blob/master/04_model_training.ipynb) by Kevin Markham
254 | - [Video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4)
255 | - NoteBook: [Comparing Machine Learning Models in Scikit-Learn](https://github.com/justmarkham/scikit-learn-videos/blob/master/05_model_evaluation.ipynb) by Kevin Markham
256 | - [Video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5)
257 | - Infographic: [K-Nearest Neighbours (100 Days Of ML Code)](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg) by Avik Jain
258 | 259 | ``` 260 | R (Programming Language): 261 | ``` 262 | 263 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
264 | * Blog: [Knn Classifier Implementation in R with Caret Package](http://dataaspirant.com/2017/01/09/knn-implementation-r-using-caret-package/) by Rahul Saxena
265 | 266 | 11. ### Ensemble Methods 267 | 268 | ``` 269 | Required Reading: 270 | ``` 271 | 272 | - Chapter 10 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) and Chapter 8 of [An Introduction to Statistical Learning: with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/)
273 | - **Exercises:** 10.1, 10.3, 10.4, and 10.5 from [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
274 | - Slide: [Bagging and Random Forests](https://davidrosenberg.github.io/mlcourse/Archive/2017/Lectures/9a.bagging-random-forests.pdf) by David Rosenberg
275 | - Slide: [Ensemble Learning through Diversity Management: Theory, Algorithms, and Applications](http://staff.ustc.edu.cn/~hchen/tutorial/IJCNN2011.pdf) by Huanhuan Chen and Xin Yao
276 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
277 | - Slide: [Introduction to Machine Learning (Boosting)](http://www.cs.huji.ac.il/~shais/Lectures2014/lecture4.pdf) by Shai Shalev-Shwartz
278 | - Paper: [Ensemble Methods in Machine Learnin](http://web.engr.oregonstate.edu/~tgd/publications/mcs-ensembles.pdf) by Thomas G. Dietterich
279 | - NoteBook: [AdaBoost](https://github.com/hhaji/Applied-Machine-Learning/blob/master/NoteBooks/AdaBoost.ipynb)
280 | 281 | ``` 282 | Additional Reading: 283 | ``` 284 | 285 | - Blog: [Ensemble Methods](https://www.datavedas.com/ensemble-methods/) by Rai Kapil
286 | - Blog: [Boosting, Bagging, and Stacking — Ensemble Methods with sklearn and mlens](https://medium.com/@rrfd/boosting-bagging-and-stacking-ensemble-methods-with-sklearn-and-mlens-a455c0c982de) by Robert R.F. DeFilippi
287 | - [NoteBook](https://github.com/robertdefilippi/ensemble-bagging-boosting/blob/master/binning-bagging.ipynb) 288 | - NoteBook: [Introduction to Python Ensembles](https://www.dataquest.io/blog/introduction-to-ensembles/) by Sebastian Flennerhag
289 | - [Library (ML-Ensemble):](http://ml-ensemble.com/docs/ensemble.html) Graph handles for deep computational graphs and ready-made ensemble classes for ensemble networks by Sebastian Flennerhag
290 | - NoteBook: [Ensemble Methods](https://github.com/vsmolyakov/experiments_with_python/blob/master/chp01/ensemble_methods.ipynb) by Vadim Smolyakov
291 | - Paper: [On Agnostic Boosting and Parity Learning](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/2008-On_Agnostic_Boosting-and_Parity_Learning.pdf) by A. T. Kalai, Y. Mansour, and E. Verbin
292 | 293 | ``` 294 | R (Programming Language): 295 | ``` 296 | 297 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
298 | * Blog: [Random Forests](http://uc-r.github.io/random_forests) by UC Business Analytics R Programming Guide
299 | 300 | 12. ### Model Selection and Validation 301 | 302 | ``` 303 | Required Reading: 304 | ``` 305 | 306 | - Chapter 11 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
307 | - **Exercises:** 11.1 and 11.2 from [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
308 | - Tutorial: [Learning Curves for Machine Learning in Python](https://www.dataquest.io/blog/learning-curves-machine-learning/) by Alex Olteanu
309 | - Blog: [K-Fold and Other Cross-Validation Techniques](https://medium.com/datadriveninvestor/k-fold-and-other-cross-validation-techniques-6c03a2563f1e) by Renu Khandelwal
310 | - NoteBook: [Split the Dataset Using Stratified K-Folds Cross-Validator](https://github.com/hhaji/Applied-Machine-Learning/blob/master/NoteBooks/Stratified-K-Folds-Cross-Validator.ipynb) 311 | - Blog: [Hyperparameter Tuning the Random Forest in Python](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74) by Will Koehrsen 312 | - [Jupyter NoteBook](https://github.com/WillKoehrsen/Machine-Learning-Projects/blob/master/random_forest_explained/Improving%20Random%20Forest%20Part%202.ipynb)
313 | - Blog: [Hyperparameter Optimization: Explanation of Automatized Algorithms](http://dkopczyk.quantee.co.uk/hyperparameter-optimization/#easy-footnote-bottom-1-379) by Dawid Kopczyk
314 | - [Code (Python):](https://github.com/dawidkopczyk/blog/blob/master/hyperparam.py) 315 | 316 | ``` 317 | Additional Reading: 318 | ``` 319 | 320 | - NoteBook: [Cross Validation](https://www.ritchieng.com/machine-learning-cross-validation/) by Ritchie Ng
321 | - NoteBook: [Cross Validation With Parameter Tuning Using Grid Search](https://chrisalbon.com/machine_learning/model_evaluation/cross_validation_parameter_tuning_grid_search/) by Chris Albon
322 | - Blog: [Random Test/Train Split is not Always Enough](http://www.win-vector.com/blog/2015/01/random-testtrain-split-is-not-always-enough/) by Win-Vector
323 | - Slide: [Cross-Validation: What, How and Which?](https://www.humanbrainmapping.org/files/2017/ED%20Courses/Course%20Materials/Crossvalidation_Raamana_PradeepReddy.pdf) by Pradeep Reddy Raamana
324 | - Paper: [Algorithms for Hyper-Parameter Optimization (NIPS 2011)](http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf) by J. Bergstra, R. Bardenet,Y. Bengio, and B. Kégl
325 | - Library: [Yellowbrick (Machine Learning Visualization)](https://www.scikit-yb.org/en/latest/index.html) 326 | - [Learning Curve](https://www.scikit-yb.org/en/latest/api/model_selection/learning_curve.html) 327 | - [Validation Curve](https://www.scikit-yb.org/en/latest/api/model_selection/validation_curve.html) 328 | 329 | ``` 330 | R (Programming Language): 331 | ``` 332 | 333 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
334 | * Blog: [Resampling Methods](http://uc-r.github.io/resampling_methods) by UC Business Analytics R Programming Guide
335 | * Blog: [Linear Model Selection](http://uc-r.github.io/model_selection) by UC Business Analytics R Programming Guide
336 | 337 | 13. ### Neural Networks 338 | 339 | ``` 340 | Required Reading: 341 | ``` 342 | 343 | - Chapter 20 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
344 | - Slide: [Neural Networks](https://www.cs.huji.ac.il/~shais/Lectures2014/lecture10.pdf) by Shai Shalev-Shwartz
345 | - Blog: [7 Types of Neural Network Activation Functions: How to Choose?](https://missinglink.ai/guides/neural-network-concepts/7-types-neural-network-activation-functions-right/)
346 | - Blog: [Activation Functions](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#id5) 347 | - Blog: [Back-Propagation, an Introduction](https://www.offconvex.org/2016/12/20/backprop/) by Sanjeev Arora and Tengyu Ma
348 | 349 | ``` 350 | Additional Reading: 351 | ``` 352 | 353 | - Blog: [The Gradient](https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/partial-derivative-and-gradient-articles/a/the-gradient) by Khanacademy
354 | - Blog: [Activation Functions](https://towardsdatascience.com/activation-functions-b63185778794) by Dhaval Dholakia
355 | - Paper: [Why Does Deep & Cheap Learning Work So Well?](https://arxiv.org/pdf/1608.08225.pdf) by Henry W. Lin, Max Tegmark, and David Rolnick
356 | - Slide: [Basics of Neural Networks](http://www.connellybarnes.com/work/class/2016/deep_learning_graphics/03_neural_net_basics.pdf) by Connelly Barnes
357 | 358 | ``` 359 | R (Programming Language): 360 | ``` 361 | 362 | * Blog: [Classification Artificial Neural Network](http://uc-r.github.io/ann_classification) by UC Business Analytics R Programming Guide
363 | 364 | 14. ### Convex Learning Problems 365 | 366 | ``` 367 | Required Reading: 368 | ``` 369 | 370 | - Chapter 12 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
371 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
372 | 373 | ``` 374 | Additional Reading: 375 | ``` 376 | - Blog: [Escaping from Saddle Points](https://www.offconvex.org/2016/03/22/saddlepoints/) by Rong Ge
377 | 378 | 15. ### Regularization and Stability 379 | 380 | ``` 381 | Required Reading: 382 | ``` 383 | 384 | - Chapter 13 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
385 | - Slide: [Machine Learning](https://github.com/rkwitt/teaching/blob/master/SS17/ML/VO/ml.pdf) by Roland Kwitt
386 | - Blog: [L1 and L2 Regularization](https://medium.com/datadriveninvestor/l1-l2-regularization-7f1b4fe948f2) by Renu Khandelwal
387 | - Blog: [L1 Norm Regularization and Sparsity Explained for Dummies](https://medium.com/mlreview/l1-norm-regularization-and-sparsity-explained-for-dummies-5b0e4be3938a) by Shi Yan
388 | 389 | ``` 390 | Additional Resources: 391 | ``` 392 | 393 | - NoteBook: [Regularization](https://github.com/ethen8181/machine-learning/blob/master/regularization/regularization.ipynb) by Ethen
394 | 395 | ``` 396 | R (Programming Language): 397 | ``` 398 | 399 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
400 | * Blog: [Regularized Regression](http://uc-r.github.io/regularized_regression) by UC Business Analytics R Programming Guide
401 | 402 | 16. ### Support Vector Machines 403 | 404 | ``` 405 | Required Reading: 406 | ``` 407 | 408 | - Chapter 15 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning)
409 | - Slide: [Support Vector Machines and Kernel Methods](https://www.cs.huji.ac.il/~shais/Lectures2014/lecture8.pdf) by Shai Shalev-Shwartz
410 | - Blog: [Support Vector Machine (SVM)](https://towardsdatascience.com/support-vector-machines-svm-c9ef22815589) by Ajay Yadav
411 | - Blog: [Support Vector Machine vs Logistic Regression](https://towardsdatascience.com/support-vector-machine-vs-logistic-regression-94cc2975433f) by Georgios Drakos
412 | 413 | ``` 414 | Additional Reading: 415 | ``` 416 | - Infographic: [Support Vector Machines (100 Days Of ML Code)](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2012.jpg) by Avik Jain
417 | - [Markdown (NoteBook)](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%2013%20SVM.md) 418 | 419 | ``` 420 | R (Programming Language): 421 | ``` 422 | 423 | * Book: [Machine Learning Mastery With R](https://machinelearningmastery.com/machine-learning-with-r/) by Jason Brownlee
424 | * Blog: [Support Vector Machine Classifier Implementation in R with Caret Package](http://dataaspirant.com/2017/01/19/support-vector-machine-classifier-implementation-r-caret-package/) by Rahul Saxena
425 | * Blog: [Support Vector Machine](http://uc-r.github.io/svm) by UC Business Analytics R Programming Guide
426 | 427 | - ### Additional NoteBooks and Slides: 428 | * Course: [Fondations of Machine Learning](https://bloomberg.github.io/foml/#home) by David S. Rosenberg
429 | * [Python Machine Learning Book Code Repository](https://github.com/rasbt/python-machine-learning-book)
430 | * [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)
431 | * [Python code for "An Introduction to Statistical Learning with Applications in R"](https://github.com/JWarmenhoven/ISLR-python) by Jordi Warmenhoven
432 | * [iPython-NoteBooks](https://github.com/jdwittenauer/ipython-notebooks) by John Wittenauer
433 | * [Scikit-Learn Tutorial](https://github.com/jakevdp/sklearn_tutorial) by Jake Vanderplas
434 | * [Data Science Roadmap](https://github.com/estraviz/data-science-roadmap#4-machine-learning-back-to-top-) by Javier Estraviz
435 | 436 | ## Class Time and Location: 437 | Saturday and Monday 08:00-09:30 AM (Spring 2019), Room 204/1. 438 | 439 | ## Projects: 440 | Projects are programming assignments that cover the topic of this course. Any project is written by 441 | **[Jupyter Notebook](http://jupyter.org)**. Projects will require the use of Python 3.7, as well as 442 | additional Python libraries as follows. 443 | 444 | * [Python 3.7:](https://www.python.org/downloads/) An interactive, object-oriented, extensible programming language.
445 | * [NumPy:](http://www.numpy.org) A Python package for scientific computing.
446 | * [Pandas:](https://pandas.pydata.org) A Python package for high-performance, easy-to-use data structures and data analysis tools.
447 | * [Scikit-Learn:](https://scikit-learn.org/stable/) A Python package for machine learning.
448 | * [Matplotlib:](https://matplotlib.org) A Python package for 2D plotting.
449 | * [SciPy:](https://www.scipy.org) A Python package for mathematics, science, and engineering.
450 | * [IPython:](https://ipython.org) An architecture for interactive computing with Python.
451 | 452 | ### Practical Guide: 453 | * Slide: [Practical Advice for Building Machine Learning Applications](https://svivek.com/teaching/machine-learning/fall2018/slides/practical/advice.pdf) by Vivek Srikumar
454 | * Blog: [Comparison of Machine Learning Models](https://github.com/justmarkham/DAT8/blob/master/other/model_comparison.md) by Kevin Markham
455 | 456 | ### Fascinating Guide to Use Python Libraries (Machine Learning): 457 | * [Technical Notes On Using Data Science & Artificial Intelligence: To Fight For Something That Matters](https://chrisalbon.com) by Chris Albon 458 | 459 | ### Google Colab: 460 | [Google Colab](https://colab.research.google.com) is a free cloud service and it supports free GPU! 461 | - [How to Use Google Colab](https://www.geeksforgeeks.org/how-to-use-google-colab/) by Souvik Mandal 462 | - [Primer for Learning Google Colab](https://medium.com/dair-ai/primer-for-learning-google-colab-bb4cabca5dd6) 463 | - [Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch](https://www.kdnuggets.com/2018/02/google-colab-free-gpu-tutorial-tensorflow-keras-pytorch.html) 464 | 465 | ### Latex: 466 | The students can include mathematical notation within markdown cells using LaTeX in their **[Jupyter Notebooks](http://jupyter.org)**.
467 | - A Brief Introduction to LaTeX [PDF](https://www.seas.upenn.edu/~cis519/spring2018/assets/resources/latex/latex.pdf)
468 | - Math in LaTeX [PDF](https://www.seas.upenn.edu/~cis519/spring2018/assets/resources/latex/math.pdf)
469 | - Sample Document [PDF](https://www.seas.upenn.edu/~cis519/spring2018/assets/resources/latex/sample.pdf)
470 | 471 | ### Useful NoteBooks: 472 | * [Preparing and Cleaning Data for Machine Learning](https://www.dataquest.io/blog/machine-learning-preparing-data/) by Josh Devlin 473 | * [Getting Started with Kaggle: House Prices Competition](https://www.dataquest.io/blog/kaggle-getting-started/) by Adam Massachi 474 | * [Scikit-learn Tutorial: Machine Learning in Python](https://www.dataquest.io/blog/sci-kit-learn-tutorial/) by Satyabrata Pal 475 | 476 | ## Grading: 477 | * Projects and Midterm – 50% 478 | * Endterm – 50% 479 | 480 | ### Final Exam: 481 | Final Examination: Saturday 1398/03/25, 08:30-10:30 482 | 483 | ## Prerequisites: 484 | General mathematical sophistication; and a solid understanding of Algorithms, Linear Algebra, and 485 | Probability Theory, at the advanced undergraduate or beginning graduate level, or equivalent. 486 | 487 | ### Linear Algebra: 488 | - Video: Professor Gilbert Strang's [Video Lectures](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) on linear algebra. 489 | 490 | ### Probability and Statistics: 491 | - [Learn Probability and Statistics Through Interactive Visualizations:](https://seeing-theory.brown.edu/index.html#firstPage) Seeing Theory was created by Daniel Kunin while an undergraduate at Brown University. The goal of this website is to make statistics more accessible through interactive visualizations (designed using Mike Bostock’s JavaScript library D3.js). 492 | - [Statistics and Probability:](https://stattrek.com) This website provides training and tools to help you solve statistics problems quickly, easily, and accurately - without having to ask anyone for help. 493 | - Jupyter NoteBooks: [Introduction to Statistics](https://github.com/rouseguy/intro2stats) by Bargava 494 | - Video: Professor John Tsitsiklis's [Video Lectures](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/video-lectures/) on Applied Probability. 495 | - Video: Professor Krishna Jagannathan's [Video Lectures](https://nptel.ac.in/courses/108106083/) on Probability Theory. 496 | 497 | ### Discrete Mathematics: 498 | Course (Videos, Lectures, Assignments): [MIT OpenCourseWare (Discrete Mathematics)](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/index.htm) 499 | 500 | ## Topics: 501 | Have a look at some reports of [Kaggle](https://www.kaggle.com/) or Stanford students ([CS224N](http://nlp.stanford.edu/courses/cs224n/2015/), [CS224D](http://cs224d.stanford.edu/reports_2016.html)) to get some general inspiration. 502 | 503 | ## Account: 504 | It is necessary to have a [GitHub](https://github.com/) account to share your projects. It offers 505 | plans for both private repositories and free accounts. Github is like the hammer in your toolbox, 506 | therefore, you need to have it! 507 | 508 | ## Academic Honor Code: 509 | Honesty and integrity are vital elements of the academic works. All your submitted assignments must be entirely your own (or your own group's). 510 | 511 | We will follow the standard of Department of Mathematical Sciences approach: 512 | * You can get help, but you MUST acknowledge the help on the work you hand in 513 | * Failure to acknowledge your sources is a violation of the Honor Code 514 | * You can talk to others about the algorithm(s) to be used to solve a homework problem; as long as you then mention their name(s) on the work you submit 515 | * You should not use code of others or be looking at code of others when you write your own: You can talk to people but have to write your own solution/code 516 | 517 | ## Questions? 518 | I will be having office hours for this course on Monday (09:30 AM--12:00 AM). If this is not convenient, email me at hhaji@sbu.ac.ir or talk to me after class. 519 | -------------------------------------------------------------------------------- /Data-Handling/README.md: -------------------------------------------------------------------------------- 1 | # Data Handling 2 | Here we introduce several blogs related to data and data handling and also some resources of datasets. 3 | * [Improve Your Data Literacy Skills and Make the Most of Data](https://www.geckoboard.com/learn/data-literacy/) by Geckoboard Company
4 | - [Tips for Effective Data Visualization](https://www.geckoboard.com/learn/data-literacy/data-visualization-tips/)
5 | - [Common Data Mistakes to Avoid](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/)
6 | 7 | ![Poster](../Images/data-fallacies-to-avoid.jpg)
8 | 9 | 10 | Common | Data | Mistakes | to | Avoid 11 | -------- | ------- | --------| -----------| ---------- 12 | [Cherry Picking](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/cherry-picking/) | [Data Dredging](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/data-dredging/) | [Survivorship Bias](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/survivorship-bias/) | [Cobra Effect](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/cobra-effect/) | [False Causality](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/false-causality/) 13 | [Gerrymandering](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/gerrymandering/) | [Sampling Bias](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/sampling-bias/) | [Gambler's Fallacy](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/gamblers-fallacy/) | [Hawthorne Effect](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/hawthorne-effect/) | [Regression Toward the Mean](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/regression-toward-the-mean/) 14 | [Simpson's Paradox](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/simpsons-paradox/) | [McNamara Fallacy](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/mcnamara-fallacy/) | [Overfitting](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/overfitting/) | [Publication Bias](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/publication-bias/) | [Danger of Summary Metrics](https://www.geckoboard.com/learn/data-literacy/statistical-fallacies/danger-of-summary-metrics/) 15 | 16 | ## Dealing with Data 17 | * Slide: [Data Preparation](https://web.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf) by João Mendes Moreira and José Luís Borges 18 | * Slide: [Data Preprocessing](http://www.csun.edu/~twang/595DM/Slides/Week2.pdf) by Taehyung Wang 19 | * Slide: [Learning with Missing Labels](https://svivek.com/teaching/machine-learning/fall2018/slides/em/missing-labels.pdf) by Vivek Srikumar 20 | * Slide: [Data Cleaning and Data Preprocessing](https://www.mimuw.edu.pl/~son/datamining/DM/4-preprocess.pdf) by Nguyen Hung Son 21 | * Blog: [Applying Wrapper Methods in Python for Feature Selection](https://stackabuse.com/applying-wrapper-methods-in-python-for-feature-selection/) 22 | by Usman Malik 23 | * Blog: [Basics of Feature Selection with Python](https://www.kaggle.com/ar2017/basics-of-feature-selection-with-python) by Andika Rachman 24 | * Blog: [Exhaustive Feature Selector](http://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/) by Sebastian Raschka 25 | * Blog: [Need for Feature Engineering in Machine Learning](https://towardsdatascience.com/need-for-feature-engineering-in-machine-learning-897df2ed00e6) by Ashish Bansal 26 | * Blog: [Data Preprocessing](http://www.cs.ccsu.edu/~markov/ccsu_courses/DataMining-3.html) by Zdravko Markov 27 | * Blog: [How to Handle Correlated Features?](https://www.kaggle.com/reisel/how-to-handle-correlated-features) by Reinhard Sellmair 28 | * Blog: [5 Ways To Handle Missing Values In Machine Learning Datasets](https://www.analyticsindiamag.com/5-ways-handle-missing-values-machine-learning-datasets/) 29 | * Blog: [Handling Missing Data](http://www.emgo.nl/kc/handling-missing-data/) 30 | * Blog: [How to Handle Missing Data](https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4) 31 | * Blog: [7 Techniques to Handle Imbalanced Data](https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html) 32 | * Blog: [Application of Synthetic Minority Over-sampling Technique (SMOTe) for Imbalanced Datasets](https://medium.com/towards-artificial-intelligence/application-of-synthetic-minority-over-sampling-technique-smote-for-imbalanced-data-sets-509ab55cfdaf) by Navoneel Chakrabarty 33 | * Paper: [SMOTE: Synthetic Minority Over-sampling Technique](https://arxiv.org/pdf/1106.1813.pdf) by Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer 34 | * Blog: [How to Handle Imbalanced Data: An Overview](https://www.datascience.com/blog/imbalanced-data) 35 | * Blog: [Visualize Missing Data with VIM Package](https://www.datacamp.com/community/tutorials/visualize-data-vim-package) 36 | * [Ultimate Guide to Handle Big Datasets for Machine Learning Using Dask (in Python)](https://www.analyticsvidhya.com/blog/2018/08/dask-big-datasets-machine_learning-python/) 37 | 38 | ## Datasets 39 | The following resources may be helpful for those still undecided about their course projects. 40 | * [VisualData:](https://www.visualdata.io) Discover computer vision datasets
41 | * [OpenML:](https://www.openml.org) An open science platform for machine learning
42 | * [Open Datasets:](https://skymind.ai/wiki/open-datasets) A list of links to publicly available datasets for a variety of domains. 43 | * [DataHub](https://datahub.io/) has a lot of structured data in formats such as RDF and CSV. 44 | * [Datasets for Machine Learning](https://www.datasetlist.com) 45 | * [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php) 46 | * [Kaggle Datasets](https://www.kaggle.com/datasets) 47 | * [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets) 48 | * [CrowdFlower Data for Everyone library](http://www.crowdflower.com/data-for-everyone) 49 | * [Stanford Large Network Dataset Collection](https://snap.stanford.edu/data/index.html) 50 | * [Data Science Weekly](https://www.datascienceweekly.org/data-science-resources/data-science-datasets) 51 | * [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience#data-sets) 52 | * [Get Financial Data Directly Into R](https://www.quandl.com/tools/r) 53 | * [Listen Data from the Green Bank Telescope](http://seti.berkeley.edu/frb-machine/) 54 | * [Cafebazaar](https://research.cafebazaar.ir/visage/datasets/) 55 | * [25 Open Datasets for Deep Learning Every Data Scientist Must Work With](https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/) by Pranav Dar 56 | * [DigiKala](https://www.dataacademy.ir/داده-های-باز-در-دیجی-کالا) (Persian) 57 | * [Statistical Center of Iran](https://www.amar.org.ir/english/) 58 | - [Dataset (Persian)](https://www.amar.org.ir/دادهها-و-اطلاعات-آماری/هزینه-و-درامد-خانوار/هزینه-و-درامد-کل-کشور#103181018---) 59 | 60 | To know more datasets, refer to the following webpage of [KDnuggets](https://www.kdnuggets.com/index.html): 61 | * [Datasets for Data Mining and Data Science](https://www.kdnuggets.com/datasets/index.html) 62 | 63 | ## Datasets of Molecules and Their Properties 64 | 65 | - Blog: [MoleculeNet](http://moleculenet.ai/) is a benchmark specially designed for testing machine learning methods of molecular properties. As we aim to facilitate the development of molecular machine learning method, this work curates a number of dataset collections, creates a suite of software that implements many known featurizations and previously proposed algorithms. All methods and datasets are integrated as parts of the open source **DeepChem** package(MIT license). 66 | - Blog: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. 67 | - Blog: [Tox21](https://tripod.nih.gov/tox21/challenge/): The 2014 Tox21 data challenge was designed to help scientists understand the potential of the chemicals and compounds being tested through the Toxicology in the 21st Century initiative to disrupt biological pathways in ways that may result in toxic effects. The Tox21 Program (Toxicology in the 21st Century) is an ongoing collaboration among federal agencies to characterize the potential toxicity of chemicals using cells and isolated molecular targets instead of laboratory animals. 68 | 69 | ## Datasets of Graphs 70 | 71 | - Blog: [Network Repository. An Interactive Scientific Network Data Repository:](http://networkrepository.com) The first interactive data and network data repository with real-time visual analytics. Network repository is not only the first interactive repository, but also the largest network repository with thousands of donations in 30+ domains (from biological to social network data). This repository was made by Ryan A. Rossi and Nesreen K. Ahmed. 72 | - Blog: [Graph Classification:](https://paperswithcode.com/task/graph-classification/latest) The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables. 73 | - Blog: [Graph Challenge Data Sets:](https://graphchallenge.mit.edu/data-sets) Amazon is making the Graph Challenge data sets available to the community free of charge as part of the AWS Public Data Sets program. The data is being presented in several file formats, and there are a variety of ways to access it. 74 | - Blog: [The House of Graphs:](https://hog.grinvin.org) a database of interesting graphs by G. Brinkmann, K. Coolsaet, J. Goedgebeur, and H. Mélot (also see Discrete Applied Mathematics, 161(1-2): 311-314, 2013 ([DOI](http://dx.doi.org/10.1016/j.dam.2012.07.018))). 75 | * [Search for Graphs](https://hog.grinvin.org/StartSearch.action) 76 | - Blog: [A Repository of Benchmark Graph Datasets for Graph Classification](https://github.com/shiruipan/graph_datasets) by 77 | Shiruipan 78 | - Blog: [Collection and Streaming of Graph Datasets](https://www.eecs.wsu.edu/~yyao/StreamingGraphs.html) by Yibo Yao 79 | - Blog: [Big Graph Data Sets](https://lgylym.github.io/big-graph/dataset.html) by Yongming Luo 80 | - Blog: [MIVIA LDGraphs Dataset:](https://mivia.unisa.it/datasets/graph-database/mivia2-graph-database/) The MIVIA LDGraphs (MIVIA Large Dense Graphs) dataset is a new dataset for benchmarking exact graph matching algorithms. It aims to extend the MIVIA graphs dataset, widely used in the last ten years, with bigger and more dense graphs, so as to face with the problems nowadays encountered in real applications devoted for instance to bioinformatics and social network analysis. 81 | - Blog: [Datasets](https://sites.wustl.edu/neumann/research/datasets/) by Marion Neumann 82 | - Blog: [Graph Dataset](https://sites.google.com/site/xiaomengsite/research/resources/graph-dataset) by Xiao Meng 83 | - Blog: [Constructors and Databases of Graphs in Sage](http://doc.sagemath.org/html/en/reference/graphs/index.html) 84 | - Datasets in GitHub: 85 | - [Benchmark Dataset for Graph Classification:](https://github.com/FilippoMB/Benchmark_dataset_for_graph_classification) This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks by Filippo Bianchi. 86 | - [GAM:](https://github.com/benedekrozemberczki/GAM) A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018) by Benedek Rozemberczki. 87 | - [CapsGNN:](https://github.com/benedekrozemberczki/CapsGNN) A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019) by Benedek Rozemberczki. 88 | 89 | ### Tools for Creating Graphs 90 | 91 | - Package: [Networkx:](https://networkx.github.io) a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. 92 | - [Graph Generators](https://networkx.github.io/documentation/stable/reference/generators.html) 93 | - [Converting to and from Other Data Formats To NetworkX Graph](https://networkx.github.io/documentation/stable/reference/convert.html) 94 | - [Reading and Writing Graphs](https://networkx.github.io/documentation/stable/reference/readwrite/index.html) 95 | 96 | - Package: [Sage:](https://www.sagemath.org) a viable free open source alternative to Magma, Maple, Mathematica and Matlab. 97 | - [CoCalc:](https://www.sagemath.org/notebook-vs-cloud.html) an [online service](https://cocalc.com/) for running SageMath computations online to avoid your own installation of Sage. CoCalc will allow you to work with multiple persistent worksheets in Sage, IPython, LaTeX, and much, much more! 98 | - [Graph Theory in Sage](http://doc.sagemath.org/html/en/reference/graphs/index.html) 99 | 100 | 101 | ## Data Science Competition Platforms 102 | * [Kaggle](https://www.kaggle.com/datasets) 103 | * [Kaggle Competition Past Solutions](http://www.chioka.in/kaggle-competition-solutions/) 104 | * [Kaggle Past Solutions](https://ndres.me/kaggle-past-solutions/) by Eliot Andres 105 | * [The Tips and Tricks I Used to Succeed on Kaggle](https://www.dataquest.io/blog/kaggle-tips-tricks/) by Vik Paruchuri 106 | * [DrivenData](http://www.drivendata.org) 107 | * [TunedIT](http://www.tunedit.org) 108 | * [InnoCentive](https://www.innocentive.com) 109 | * [CrowdAnalytix](http://www.crowdanalytix.com) 110 | -------------------------------------------------------------------------------- /Images/DrTaheri.jpeg: 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4.6,3.4,1.4,0.3,Iris-setosa 9 | 5.0,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,3.7,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3.0,1.4,0.1,Iris-setosa 15 | 4.3,3.0,1.1,0.1,Iris-setosa 16 | 5.8,4.0,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1.0,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5.0,3.0,1.6,0.2,Iris-setosa 28 | 5.0,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.2,Iris-setosa 37 | 5.0,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.6,1.4,0.1,Iris-setosa 40 | 4.4,3.0,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5.0,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5.0,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3.0,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5.0,3.3,1.4,0.2,Iris-setosa 52 | 7.0,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4.0,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1.0,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5.0,2.0,3.5,1.0,Iris-versicolor 63 | 5.9,3.0,4.2,1.5,Iris-versicolor 64 | 6.0,2.2,4.0,1.0,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3.0,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1.0,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4.0,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3.0,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3.0,5.0,1.7,Iris-versicolor 80 | 6.0,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1.0,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1.0,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6.0,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3.0,4.5,1.5,Iris-versicolor 87 | 6.0,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3.0,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4.0,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3.0,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4.0,1.2,Iris-versicolor 95 | 5.0,2.3,3.3,1.0,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3.0,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3.0,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6.0,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3.0,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3.0,5.8,2.2,Iris-virginica 107 | 7.6,3.0,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2.0,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3.0,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5.0,2.0,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3.0,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6.0,2.2,5.0,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2.0,Iris-virginica 124 | 7.7,2.8,6.7,2.0,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6.0,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3.0,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3.0,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2.0,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3.0,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6.0,3.0,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3.0,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5.0,1.9,Iris-virginica 149 | 6.5,3.0,5.2,2.0,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /NoteBooks/README.md: -------------------------------------------------------------------------------- 1 | # NoteBooks 2 | -------------------------------------------------------------------------------- /NoteBooks/Stratified-K-Folds-Cross-Validator.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Split the Dataset Using Stratified K-Folds Cross-Validator" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import pandas as pd\n", 17 | "import numpy as np" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "df=pd.read_csv(\"Data/Titanic.csv\")" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 3, 32 | "metadata": {}, 33 | "outputs": [ 34 | { 35 | "data": { 36 | "text/html": [ 37 | "

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

\n", 147 | "

" 148 | ], 149 | "text/plain": [ 150 | " PassengerId Survived Pclass \\\n", 151 | "0 1 0 3 \n", 152 | "1 2 1 1 \n", 153 | "2 3 1 3 \n", 154 | "3 4 1 1 \n", 155 | "4 5 0 3 \n", 156 | "\n", 157 | " Name Sex Age SibSp \\\n", 158 | "0 Braund, Mr. Owen Harris male 22.0 1 \n", 159 | "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", 160 | "2 Heikkinen, Miss. Laina female 26.0 0 \n", 161 | "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", 162 | "4 Allen, Mr. William Henry male 35.0 0 \n", 163 | "\n", 164 | " Parch Ticket Fare Cabin Embarked \n", 165 | "0 0 A/5 21171 7.2500 NaN S \n", 166 | "1 0 PC 17599 71.2833 C85 C \n", 167 | "2 0 STON/O2. 3101282 7.9250 NaN S \n", 168 | "3 0 113803 53.1000 C123 S \n", 169 | "4 0 373450 8.0500 NaN S " 170 | ] 171 | }, 172 | "execution_count": 3, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "df.head()" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 4, 184 | "metadata": {}, 185 | "outputs": [ 186 | { 187 | "data": { 188 | "text/plain": [ 189 | "(891, 12)" 190 | ] 191 | }, 192 | "execution_count": 4, 193 | "metadata": {}, 194 | "output_type": "execute_result" 195 | } 196 | ], 197 | "source": [ 198 | "df.shape" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 6, 204 | "metadata": {}, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "text/plain": [ 209 | "0 549\n", 210 | "1 342\n", 211 | "Name: Survived, dtype: int64" 212 | ] 213 | }, 214 | "execution_count": 6, 215 | "metadata": {}, 216 | "output_type": "execute_result" 217 | } 218 | ], 219 | "source": [ 220 | "df.Survived.value_counts()" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 7, 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", 232 | " 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n", 233 | " dtype='object')" 234 | ] 235 | }, 236 | "execution_count": 7, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "df.columns" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 8, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "data": { 252 | "text/plain": [ 253 | "['PassengerId',\n", 254 | " 'Pclass',\n", 255 | " 'Name',\n", 256 | " 'Sex',\n", 257 | " 'Age',\n", 258 | " 'SibSp',\n", 259 | " 'Parch',\n", 260 | " 'Ticket',\n", 261 | " 'Fare',\n", 262 | " 'Cabin',\n", 263 | " 'Embarked']" 264 | ] 265 | }, 266 | "execution_count": 8, 267 | "metadata": {}, 268 | "output_type": "execute_result" 269 | } 270 | ], 271 | "source": [ 272 | "features=[i for i in df.columns if i !=\"Survived\"]\n", 273 | "features" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 9, 279 | "metadata": {}, 280 | "outputs": [], 281 | "source": [ 282 | "X=np.array(df[features])\n", 283 | "y=np.array(df[\"Survived\"])" 284 | ] 285 | }, 286 | { 287 | "cell_type": "code", 288 | "execution_count": 10, 289 | "metadata": {}, 290 | "outputs": [ 291 | { 292 | "data": { 293 | "text/plain": [ 294 | "3" 295 | ] 296 | }, 297 | "execution_count": 10, 298 | "metadata": {}, 299 | "output_type": "execute_result" 300 | } 301 | ], 302 | "source": [ 303 | "from sklearn.model_selection import StratifiedKFold \n", 304 | "size=3\n", 305 | "skf = StratifiedKFold(n_splits=size)\n", 306 | "skf.get_n_splits(X, y)" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 16, 312 | "metadata": {}, 313 | "outputs": [ 314 | { 315 | "data": { 316 | "text/plain": [ 317 | "" 318 | ] 319 | }, 320 | "execution_count": 16, 321 | "metadata": {}, 322 | "output_type": "execute_result" 323 | } 324 | ], 325 | "source": [ 326 | "# Note that skf.split(X, y) is a generator\n", 327 | "folds_indices=skf.split(X, y)\n", 328 | "folds_indices" 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": 17, 334 | "metadata": {}, 335 | "outputs": [], 336 | "source": [ 337 | "# Generate the First Training Set and Test Set\n", 338 | "train_index, test_index=next(folds_indices)\n", 339 | "X_train, X_test = X[train_index], X[test_index]\n", 340 | "y_train, y_test = y[train_index], y[test_index]" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": null, 346 | "metadata": {}, 347 | "outputs": [], 348 | "source": [ 349 | "## Generate all Training and Test Sets\n", 350 | "# L_data=list()\n", 351 | "# L_target=list()\n", 352 | "# for i in range(0,size):\n", 353 | "# train_index, test_index=next(folds_indices)\n", 354 | "# L_data.append((X[train_index], X[test_index]))\n", 355 | "# L_target.append((y[train_index], y[test_index]))\n", 356 | "## Note that it is not necessary to save the data within a list! Indeed, we can recall training and test \n", 357 | "## sets using the next command when we need them. " 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "## Count Unique Values Using Numpy" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": 19, 370 | "metadata": {}, 371 | "outputs": [ 372 | { 373 | "name": "stdout", 374 | "output_type": "stream", 375 | "text": [ 376 | "[[ 0 366]\n", 377 | " [ 1 228]]\n" 378 | ] 379 | } 380 | ], 381 | "source": [ 382 | "unique_1, counts_1 = np.unique(y_train, return_counts=True)\n", 383 | "print(np.asarray((unique_1, counts_1)).T)" 384 | ] 385 | }, 386 | { 387 | "cell_type": "code", 388 | "execution_count": 20, 389 | "metadata": {}, 390 | "outputs": [ 391 | { 392 | "name": "stdout", 393 | "output_type": "stream", 394 | "text": [ 395 | "[[ 0 183]\n", 396 | " [ 1 114]]\n" 397 | ] 398 | } 399 | ], 400 | "source": [ 401 | "unique_2, counts_2 = np.unique(y_test, return_counts=True)\n", 402 | "print(np.asarray((unique_2, counts_2)).T)" 403 | ] 404 | }, 405 | { 406 | "cell_type": "markdown", 407 | "metadata": {}, 408 | "source": [ 409 | "## Count Unique Values Using Pandas" 410 | ] 411 | }, 412 | { 413 | "cell_type": "code", 414 | "execution_count": 22, 415 | "metadata": {}, 416 | "outputs": [ 417 | { 418 | "data": { 419 | "text/html": [ 420 | "

\n", 421 | "\n", 434 | "\n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | "

	PassengerId	Pclass	Name	Sex	Age	Ticket	Fare	Cabin	Embarked
0	285	1	Smith, Mr. Richard William	male	NaN	113056	26	A19	S
1	286	3	Stankovic, Mr. Ivan	male	33	349239	8.6625	NaN	C
2	288	3	Naidenoff, Mr. Penko	male	22	349206	7.8958	NaN	S
3	293	2	Levy, Mr. Rene Jacques	male	36	SC/Paris 2163	12.875	D	C
4	294	3	Haas, Miss. Aloisia	female	24	349236	8.85	NaN	S

\n", 524 | "

" 525 | ], 526 | "text/plain": [ 527 | " PassengerId Pclass Name Sex Age SibSp Parch \\\n", 528 | "0 285 1 Smith, Mr. Richard William male NaN 0 0 \n", 529 | "1 286 3 Stankovic, Mr. Ivan male 33 0 0 \n", 530 | "2 288 3 Naidenoff, Mr. Penko male 22 0 0 \n", 531 | "3 293 2 Levy, Mr. Rene Jacques male 36 0 0 \n", 532 | "4 294 3 Haas, Miss. Aloisia female 24 0 0 \n", 533 | "\n", 534 | " Ticket Fare Cabin Embarked \n", 535 | "0 113056 26 A19 S \n", 536 | "1 349239 8.6625 NaN C \n", 537 | "2 349206 7.8958 NaN S \n", 538 | "3 SC/Paris 2163 12.875 D C \n", 539 | "4 349236 8.85 NaN S " 540 | ] 541 | }, 542 | "execution_count": 22, 543 | "metadata": {}, 544 | "output_type": "execute_result" 545 | } 546 | ], 547 | "source": [ 548 | "df_train=pd.DataFrame(X_train, columns=features)\n", 549 | "df_train.head()" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": 23, 555 | "metadata": {}, 556 | "outputs": [], 557 | "source": [ 558 | "df_train=pd.DataFrame(X_train, columns=features)\n", 559 | "df_test=pd.DataFrame(X_test, columns=features)\n", 560 | "df_target_train=pd.DataFrame(y_train, columns=[\"Survived\"])\n", 561 | "df_target_test=pd.DataFrame(y_test, columns=[\"Survived\"])" 562 | ] 563 | }, 564 | { 565 | "cell_type": "code", 566 | "execution_count": 25, 567 | "metadata": {}, 568 | "outputs": [ 569 | { 570 | "data": { 571 | "text/plain": [ 572 | "0 366\n", 573 | "1 228\n", 574 | "Name: Survived, dtype: int64" 575 | ] 576 | }, 577 | "execution_count": 25, 578 | "metadata": {}, 579 | "output_type": "execute_result" 580 | } 581 | ], 582 | "source": [ 583 | "df_target_train.Survived.value_counts()" 584 | ] 585 | }, 586 | { 587 | "cell_type": "code", 588 | "execution_count": 26, 589 | "metadata": {}, 590 | "outputs": [ 591 | { 592 | "data": { 593 | "text/plain": [ 594 | "0 183\n", 595 | "1 114\n", 596 | "Name: Survived, dtype: int64" 597 | ] 598 | }, 599 | "execution_count": 26, 600 | "metadata": {}, 601 | "output_type": "execute_result" 602 | } 603 | ], 604 | "source": [ 605 | "df_target_test.Survived.value_counts()" 606 | ] 607 | }, 608 | { 609 | "cell_type": "markdown", 610 | "metadata": {}, 611 | "source": [ 612 | "## Generate the Second Training Set and Test Set" 613 | ] 614 | }, 615 | { 616 | "cell_type": "code", 617 | "execution_count": 27, 618 | "metadata": {}, 619 | "outputs": [ 620 | { 621 | "data": { 622 | "text/html": [ 623 | "

\n", 624 | "\n", 637 | "\n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | "

	PassengerId	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	3	Braund, Mr. Owen Harris	male	22	1	A/5 21171	7.25	NaN	S
1	2	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38	1	PC 17599	71.2833	C85	C
2	3	3	Heikkinen, Miss. Laina	female	26	0	STON/O2. 3101282	7.925	NaN	S
3	4	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35	1	113803	53.1	C123	S
4	5	3	Allen, Mr. William Henry	male	35	0	373450	8.05	NaN	S

\n", 727 | "

" 728 | ], 729 | "text/plain": [ 730 | " PassengerId Pclass Name \\\n", 731 | "0 1 3 Braund, Mr. Owen Harris \n", 732 | "1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", 733 | "2 3 3 Heikkinen, Miss. Laina \n", 734 | "3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", 735 | "4 5 3 Allen, Mr. William Henry \n", 736 | "\n", 737 | " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", 738 | "0 male 22 1 0 A/5 21171 7.25 NaN S \n", 739 | "1 female 38 1 0 PC 17599 71.2833 C85 C \n", 740 | "2 female 26 0 0 STON/O2. 3101282 7.925 NaN S \n", 741 | "3 female 35 1 0 113803 53.1 C123 S \n", 742 | "4 male 35 0 0 373450 8.05 NaN S " 743 | ] 744 | }, 745 | "execution_count": 27, 746 | "metadata": {}, 747 | "output_type": "execute_result" 748 | } 749 | ], 750 | "source": [ 751 | "train_index, test_index=next(folds_indices)\n", 752 | "X_train, X_test = X[train_index], X[test_index]\n", 753 | "y_train, y_test = y[train_index], y[test_index]\n", 754 | "df_train=pd.DataFrame(X_train, columns=features)\n", 755 | "df_train.head()" 756 | ] 757 | } 758 | ], 759 | "metadata": { 760 | "kernelspec": { 761 | "display_name": "Python 3", 762 | "language": "python", 763 | "name": "python3" 764 | }, 765 | "language_info": { 766 | "codemirror_mode": { 767 | "name": "ipython", 768 | "version": 3 769 | }, 770 | "file_extension": ".py", 771 | "mimetype": "text/x-python", 772 | "name": "python", 773 | "nbconvert_exporter": "python", 774 | "pygments_lexer": "ipython3", 775 | "version": "3.6.8" 776 | } 777 | }, 778 | "nbformat": 4, 779 | "nbformat_minor": 2 780 | } 781 | -------------------------------------------------------------------------------- /Projects/Projects-Fall-2021/Data/Global-Features/Readme.md: -------------------------------------------------------------------------------- 1 | 2 | # Global features of datasets 3 | 4 | [qm9_global_cdf_rdkit.zip](https://drive.google.com/file/d/1Gj6u_FqZQ4cFogPWUfzwPZeCniCqBchj/view?usp=sharing) 5 | 6 | [hiv_global_cdf_rdkit.zip](https://drive.google.com/file/d/1-5xW1HKD87uaWsfeh8TUgDrUcPFLEG96/view?usp=sharing) 7 | 8 | [muv_global_cdf_rdkit.zip](https://drive.google.com/file/d/1zsR1lGewCwhD7a9ENkDlqOi7e9bmdjQF/view?usp=sharing) 9 | 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https://raw.githubusercontent.com/hhaji/Applied-Machine-Learning/dab9c127e2a9f05b964f194b3d1264f68c3029ec/Projects/Projects-Fall-2021/Data/Global-Features/toxcast_global_cdf_rdkit.zip -------------------------------------------------------------------------------- /Projects/Projects-Fall-2021/Data/Readme.md: -------------------------------------------------------------------------------- 1 | # [Dataset Collection](https://moleculenet.org/datasets-1) 2 | -------------------------------------------------------------------------------- /Projects/Projects-Fall-2021/Data/freesolv.csv: -------------------------------------------------------------------------------- 1 | smiles,freesolv 2 | CN(C)C(=O)c1ccc(cc1)OC,-11.01 3 | CS(=O)(=O)Cl,-4.87 4 | CC(C)C=C,1.83 5 | CCc1cnccn1,-5.45 6 | CCCCCCCO,-4.21 7 | Cc1cc(cc(c1)O)C,-6.27 8 | CC(C)C(C)C,2.34 9 | CCCC(C)(C)O,-3.92 10 | C[C@@H]1CCCC[C@@H]1C,1.58 11 | CC[C@H](C)O,-4.62 12 | C(Br)Br,-1.96 13 | CC[C@H](C(C)C)O,-3.88 14 | CCc1ccccn1,-4.33 15 | CCCCC(=O)OCC,-2.49 16 | c1ccc(cc1)S,-2.55 17 | CC(=CCC/C(=C\CO)/C)C,-4.78 18 | c1ccc2c(c1)CCC2,-1.46 19 | CCOc1ccccc1,-2.22 20 | c1cc(ccc1O)Br,-5.85 21 | CCCC(C)(C)C,2.88 22 | CC(=O)OCCOC(=O)C,-6.34 23 | CCOP(=S)(OCC)SCSP(=S)(OCC)OCC,-6.1 24 | C1CCCC(CC1)O,-5.48 25 | COC(=O)C1CC1,-4.1 26 | c1ccc(cc1)C#N,-4.1 27 | CCCCC#N,-3.52 28 | CC(C)(C)O,-4.47 29 | CC(C)C(=O)C(C)C,-2.74 30 | CCC=O,-3.43 31 | CN(C)C=O,-7.81 32 | Cc1ccc(cc1)C,-0.8 33 | C=CCC=C,0.93 34 | Cc1cccc(c1C)Nc2ccccc2C(=O)O,-6.78 35 | CN(C)C(=O)c1ccccc1,-9.29 36 | CCNCC,-4.07 37 | CC(C)(C)c1ccc(cc1)O,-5.91 38 | CC(C)CCOC=O,-2.13 39 | CCCCCCCCCCO,-3.64 40 | CCC(=O)OCC,-2.68 41 | CCCCCCCCC,3.13 42 | CC(=O)NC,-10 43 | CCCCCCCC=C,2.06 44 | c1ccc2cc(ccc2c1)O,-8.11 45 | c1cc(c(cc1Cl)Cl)Cl,-1.12 46 | C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O,-23.62 47 | CCCC(=O)OC,-2.83 48 | c1ccc(c(c1)C=O)O,-4.68 49 | C1CNC1,-5.56 50 | CCCNCCC,-3.65 51 | c1ccc(cc1)N,-5.49 52 | C(F)(F)(F)F,3.12 53 | CC[C@@H](C)CO,-4.42 54 | c1ccc(c(c1)O)I,-6.2 55 | COc1cccc(c1O)OC,-6.96 56 | CCC#C,-0.16 57 | c1ccc(cc1)C(F)(F)F,-0.25 58 | NN,-9.3 59 | Cc1ccccn1,-4.63 60 | CCNc1nc(nc(n1)Cl)NCC,-10.22 61 | c1ccc2c(c1)Oc3cc(c(cc3O2)Cl)Cl,-3.56 62 | CCCCCCCCN,-3.65 63 | N,-4.29 64 | c1ccc(c(c1)C(F)(F)F)C(F)(F)F,1.07 65 | COC(=O)c1ccc(cc1)O,-9.51 66 | CCCCCc1ccccc1,-0.23 67 | CC(F)F,-0.11 68 | c1ccc(cc1)n2c(=O)c(c(cn2)N)Cl,-16.43 69 | C=CC=C,0.56 70 | CN(C)C,-3.2 71 | CCCCCC(=O)N,-9.31 72 | CC(C)CO[N+](=O)[O-],-1.88 73 | c1ccc2c(c1)C(=O)c3cccc(c3C2=O)NCCO,-14.21 74 | C(CO[N+](=O)[O-])O,-8.18 75 | CCCCCCC(=O)C,-2.88 76 | CN1CCNCC1,-7.77 77 | CCN,-4.5 78 | C1C=CC=CC=C1,-0.99 79 | c1ccc2c(c1)Cc3ccccc3C2,-3.78 80 | CC(Cl)Cl,-0.84 81 | COc1cccc(c1)O,-7.66 82 | c1cc2cccc3c2c(c1)CC3,-3.15 83 | CCCCCCCCBr,0.52 84 | c1ccc(cc1)CO,-6.62 85 | c1c(c(=O)[nH]c(=O)[nH]1)Br,-18.17 86 | CCCC,2.1 87 | CCl,-0.55 88 | CC(C)CBr,-0.03 89 | CC(C)SC(C)C,-1.21 90 | CCCCCCC,2.67 91 | c1cnc[nH]1,-9.63 92 | c1cc2c(cc1Cl)Oc3cc(c(c(c3O2)Cl)Cl)Cl,-3.84 93 | CC[C@H](C)n1c(=O)c(c([nH]c1=O)C)Br,-9.73 94 | C(I)I,-2.49 95 | CCCN(CCC)C(=O)SCCC,-4.13 96 | C[N+](=O)[O-],-4.02 97 | CCOC,-2.1 98 | COC(CCl)(OC)OC,-4.59 99 | CC(C)C,2.3 100 | CC(C)CC(=O)O,-6.09 101 | CCOP(=O)(OCC)O/C(=C/Cl)/c1ccc(cc1Cl)Cl,-7.07 102 | CCCCl,-0.33 103 | CCCSCCC,-1.28 104 | CCC[C@H](CC)O,-4.06 105 | CC#N,-3.88 106 | CN(CC(F)(F)F)c1ccccc1,-1.92 107 | [C@@H](C(F)(F)F)(OC(F)F)Cl,0.1 108 | C=CCCC=C,1.01 109 | Cc1cccc(c1)C,-0.83 110 | CC(=O)OC,-3.13 111 | COC(c1ccccc1)(OC)OC,-4.04 112 | CCOC(=O)c1ccccc1,-3.64 113 | CCCS,-1.1 114 | CCCCCC(=O)C,-3.04 115 | CC1(Cc2cccc(c2O1)OC(=O)NC)C,-9.61 116 | c1ccc(cc1)CBr,-2.38 117 | CCCCCC(=O)OCC,-2.23 118 | CCCOC,-1.66 119 | CN1CCOCC1,-6.32 120 | c1cc(cc(c1)O)C#N,-9.65 121 | c1cc(c(cc1c2c(c(cc(c2Cl)Cl)Cl)Cl)Cl)Cl,-4.38 122 | CCCc1ccccc1,-0.53 123 | Cn1cnc2c1c(=O)n(c(=O)n2C)C,-12.64 124 | CNC,-4.29 125 | C(=C(F)F)(C(F)(F)F)F,2.93 126 | c1cc(ccc1O)Cl,-7.03 127 | C1CCNCC1,-5.11 128 | c1ccc2c(c1)ccc3c2cccc3,-3.88 129 | CI,-0.89 130 | COc1c(cc(c(c1O)OC)Cl)Cl,-6.44 131 | C(=C/Cl)\Cl,-0.78 132 | CCCCC,2.3 133 | CCCC#N,-3.64 134 | [C@@H](C(F)(F)F)(F)Br,0.5 135 | CC(C)Cc1cnccn1,-5.04 136 | CC[C@H](C)O[N+](=O)[O-],-1.82 137 | c1ccc(cc1)c2cc(ccc2Cl)Cl,-2.46 138 | c1ccc(cc1)c2cc(c(c(c2Cl)Cl)Cl)Cl,-3.48 139 | CC[C@@H](C)C(C)C,2.52 140 | C[C@H](CC(C)C)O,-3.73 141 | C1CCOCC1,-3.12 142 | C1CC1,0.75 143 | c1c(cc(c(c1Cl)Cl)Cl)c2cc(c(c(c2Cl)Cl)Cl)Cl,-3.17 144 | C=C(Cl)Cl,0.25 145 | CC(C)CO,-4.5 146 | CCCOC(=O)CC,-2.44 147 | C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl,-0.64 148 | CSc1ccccc1,-2.73 149 | CCc1ccccc1O,-5.66 150 | CC(C)(C)Cl,1.09 151 | CC(=C)C=C,0.68 152 | Cc1ccc(cc1)C(C)C,-0.68 153 | Cn1ccnc1,-8.41 154 | C(CO)O,-9.3 155 | c1ccc(c(c1)Cl)Cl,-1.36 156 | c1c(=O)[nH]c(=O)[nH]c1Cl,-15.83 157 | CCCOC=O,-2.48 158 | c1ccc2c(c1)Oc3ccc(cc3O2)Cl,-3.1 159 | CCCCCC(=O)O,-6.21 160 | CCOC(=O)CCC(=O)OCC,-5.71 161 | Cc1ccnc(c1)C,-4.86 162 | C1CCC=CC1,0.14 163 | CN1CCN(CC1)C,-7.58 164 | c1cc(c(cc1c2cc(c(c(c2Cl)Cl)Cl)Cl)Cl)Cl,-3.04 165 | C1=CC(=O)C=CC1=O,-6.5 166 | COC(=O)CCl,-4 167 | CCCC=O,-3.18 168 | CCc1ccccc1,-0.79 169 | C(=C(Cl)Cl)Cl,-0.44 170 | CCN(CC)CC,-3.22 171 | c1cc2c(cc1Cl)Oc3c(c(c(c(c3Cl)Cl)Cl)Cl)O2,-4.15 172 | Cc1ccncc1C,-5.22 173 | c1(=O)[nH]c(=O)[nH]c(=O)[nH]1,-18.06 174 | c1ccc(cc1)C=O,-4.02 175 | c1ccnc(c1)Cl,-4.39 176 | C=CCCl,-0.57 177 | Cc1ccc(cc1)C(=O)C,-4.7 178 | C=O,-2.75 179 | Cc1ccccc1Cl,-1.14 180 | CC(=O)N1CCCC1,-9.8 181 | CC(OC)(OC)OC,-4.42 182 | CCCCc1ccccc1,-0.4 183 | CN(C)c1ccccc1,-3.45 184 | CC(C)OC,-2.01 185 | c12c(c(c(c(c1Cl)Cl)Cl)Cl)Oc3c(c(c(c(c3Cl)Cl)Cl)Cl)O2,-4.53 186 | c1(c(c(c(c(c1Cl)Cl)Cl)Cl)Cl)c2c(c(c(c(c2Cl)Cl)Cl)Cl)Cl,-2.98 187 | C(C(Cl)Cl)Cl,-1.99 188 | CNc1ccccc1,-4.69 189 | CC(C)OC(=O)C,-2.64 190 | c1ccccc1,-0.9 191 | c1cc(c(c(c1)Cl)Cl)Cl,-1.24 192 | CCOP(=S)(OCC)SCSc1ccc(cc1)Cl,-6.5 193 | COP(=S)(OC)SCn1c(=O)c2ccccc2nn1,-10.03 194 | c1ccc2c(c1)Oc3c(cc(c(c3O2)Cl)Cl)Cl,-4.05 195 | CC(=C)C(=C)C,0.4 196 | CCCCC=C,1.58 197 | S,-0.7 198 | CCOCC,-1.59 199 | CCNc1nc(nc(n1)SC)NC(C)C,-7.65 200 | CCCCOC(=O)c1ccc(cc1)O,-8.72 201 | CCCCCCOC(=O)C,-2.26 202 | C1CCC(=O)C1,-4.7 203 | CCCCC(=O)O,-6.16 204 | CCBr,-0.74 205 | Cc1ccc2cc(ccc2c1)C,-2.63 206 | CCCCCCO,-4.4 207 | c1ccc(cc1)c2ccccc2Cl,-2.69 208 | CC1=CCCCC1,0.67 209 | CCCCCCO[N+](=O)[O-],-1.66 210 | C(Br)(Br)Br,-2.13 211 | CCc1ccc(cc1)O,-6.13 212 | CCCOCCO,-6.4 213 | c1ccc(cc1)OC=O,-3.82 214 | c1c(c(=O)[nH]c(=O)[nH]1)I,-18.72 215 | CCCC(=O)O,-6.35 216 | COC(C(F)(F)F)(OC)OC,-0.8 217 | C1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O,-20.52 218 | C(F)(F)(F)Br,1.79 219 | CCCCO,-4.72 220 | c1ccc(cc1)F,-0.8 221 | CCOC(=O)C,-2.94 222 | CC(C)COC(=O)C(C)C,-1.69 223 | CC(C)(C)OC,-2.21 224 | C1=C[C@@H]([C@@H]2[C@H]1[C@@]3(C(=C([C@]2(C3(Cl)Cl)Cl)Cl)Cl)Cl)Cl,-2.55 225 | CCC(=O)CC,-3.41 226 | COC(=O)C(F)(F)F,-1.1 227 | c1ccc2ccccc2c1,-2.4 228 | c1cc(c(c(c1c2cc(c(c(c2Cl)Cl)Cl)Cl)Cl)Cl)Cl,-4.4 229 | CC(=O)Oc1ccccc1C(=O)O,-9.94 230 | CC(=O)C(C)(C)C,-3.11 231 | COS(=O)(=O)C,-4.87 232 | CCc1ccncc1,-4.73 233 | CC(C)NC(C)C,-3.22 234 | c1cc2c(cc1Cl)Oc3ccc(cc3O2)Cl,-3.67 235 | CCCCCCCN,-3.79 236 | CC1CCCC1,1.59 237 | CCC,2 238 | C[C@H]1CCCO1,-3.3 239 | CNC(=O)Oc1cccc2c1cccc2,-9.45 240 | c1cc(cc(c1)O)C=O,-9.52 241 | c1ccc2cc3ccccc3cc2c1,-3.95 242 | C(Cl)Cl,-1.31 243 | CC(C)(C)C(=O)OC,-2.4 244 | C([N+](=O)[O-])(Cl)(Cl)Cl,-1.45 245 | C1CC[S+2](C1)([O-])[O-],-8.61 246 | Cc1cccc(c1O)C,-5.26 247 | Cc1cccc(c1)O,-5.49 248 | c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)O)N,-9.53 249 | c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)N)N,-11.85 250 | CCCCCCCC(=O)C,-2.49 251 | CCCCN,-4.24 252 | CCCC(=O)OCC,-2.49 253 | Cc1ccc(cc1)N,-5.57 254 | CCCCCCI,0.08 255 | C(C(F)(Cl)Cl)(F)(F)Cl,1.77 256 | COP(=O)(OC)OC,-8.7 257 | c1cc(cc(c1)Cl)Cl,-0.98 258 | Cc1cc(c2ccccc2c1)C,-2.47 259 | CCCC(C)C,2.51 260 | CCOP(=S)(OCC)Oc1c(cc(c(n1)Cl)Cl)Cl,-5.04 261 | C(C(F)(F)F)Cl,0.06 262 | C=C,1.28 263 | CCCCCI,-0.14 264 | COC(OC)OC,-4.42 265 | CCCCCCCCCC,3.16 266 | C[C@@H](CO[N+](=O)[O-])O[N+](=O)[O-],-4.95 267 | CC=C,1.32 268 | Cc1c[nH]c2c1cccc2,-5.88 269 | COP(=O)([C@H](C(Cl)(Cl)Cl)O)OC,-12.74 270 | C1CCCCC1,1.23 271 | CC(=CCC/C(=C/CO)/C)C,-4.45 272 | CC(C)c1ccccc1,-0.3 273 | CC(C)C(C)C(C)C,2.56 274 | CC(C)C(=O)C,-3.24 275 | CCCCNCCCC,-3.24 276 | CCCCS,-0.99 277 | c1ccc2c(c1)Oc3c(c(c(c(c3Cl)Cl)Cl)Cl)O2,-3.81 278 | COc1c(c(c(c(c1Cl)C=O)Cl)OC)O,-8.68 279 | C1CCC(CC1)N,-4.59 280 | C(F)(F)Cl,-0.5 281 | COC(=O)c1ccc(cc1)[N+](=O)[O-],-6.88 282 | CC(=O)c1cccnc1,-8.26 283 | CC#C,-0.48 284 | CCCCCCCCC=O,-2.07 285 | CCC(=O)O,-6.46 286 | C(Cl)(Cl)Cl,-1.08 287 | Cc1cccc(c1C)C,-1.21 288 | C,2 289 | c1ccc(cc1)CCl,-1.93 290 | CC1CCCCC1,1.7 291 | Cc1cccs1,-1.38 292 | c1ccncc1,-4.69 293 | CCCCCl,-0.16 294 | C[C@H]1CC[C@@H](O1)C,-2.92 295 | Cc1ccc(c(c1)OC)O,-5.8 296 | C1[C@H]([C@@H]2[C@H]([C@H]1Cl)[C@]3(C(=C([C@@]2(C3(Cl)Cl)Cl)Cl)Cl)Cl)Cl,-3.44 297 | Cc1ccccc1,-0.9 298 | CC(C)COC=O,-2.22 299 | CCOC(=O)c1ccc(cc1)O,-9.2 300 | CCOCCOCC,-3.54 301 | CCCCCOC(=O)CC,-2.11 302 | CCCc1ccc(cc1)O,-5.21 303 | CC=C(C)C,1.31 304 | C(CCl)Cl,-1.79 305 | CCC(C)(C)CC,2.56 306 | Cc1cc2ccccc2cc1C,-2.78 307 | Cc1cccc(n1)C,-4.59 308 | COC(C(Cl)Cl)(F)F,-1.12 309 | CCOCCOC(=O)C,-5.31 310 | COc1cccc(c1)N,-7.29 311 | c1cc(cnc1)C=O,-7.1 312 | CCC(C)(C)O,-4.43 313 | CCc1cccc(c1N(COC)C(=O)CCl)CC,-8.21 314 | Cn1cccc1,-2.89 315 | COCOC,-2.93 316 | CCC(CC)O,-4.35 317 | CCCCCCCCCC(=O)C,-2.15 318 | C(CBr)Cl,-1.95 319 | c1ccc(cc1)I,-1.74 320 | CC1=CC(=O)CC(C1)(C)C,-5.18 321 | CCI,-0.74 322 | CCCc1ccc(c(c1)OC)O,-5.26 323 | CC(C)Br,-0.48 324 | Cc1ccc(cc1)Br,-1.39 325 | c1cc(ccc1C#N)O,-10.17 326 | CS(=O)(=O)C,-10.08 327 | CCc1cccc(c1)O,-6.25 328 | CC1=CC[C@H](C[C@@H]1O)C(=C)C,-4.44 329 | c1cc(ccc1Br)Br,-2.3 330 | COc1c(ccc(c1C(=O)O)Cl)Cl,-9.86 331 | CC/C=C\C,1.31 332 | CC,1.83 333 | COc1ccccc1OC,-5.33 334 | CCSCC,-1.46 335 | c1cc(cnc1)C#N,-6.75 336 | c1cc(c(cc1O)Cl)Cl,-7.29 337 | COc1ccccc1,-2.45 338 | Cc1ccc(c(c1)O)C,-5.91 339 | c1cc(ccc1Cl)Cl,-1.01 340 | C(F)Cl,-0.77 341 | CCCC=C,1.68 342 | c1cc(c(c(c1Cl)Cl)Cl)Cl,-1.34 343 | CCCCCC#C,0.6 344 | CCCCCCCCC(=O)C,-2.34 345 | c1ccc(cc1)Cl,-1.12 346 | CN(C)CCOC(c1ccccc1)c2ccccc2,-9.34 347 | CCCCC=O,-3.03 348 | c1ccc(cc1)Oc2ccccc2,-2.87 349 | C1CCC(=O)CC1,-4.91 350 | CCCC[N+](=O)[O-],-3.09 351 | c1cnccc1C=O,-7 352 | C(CCl)OCCCl,-4.23 353 | CC[N+](=O)[O-],-3.71 354 | c1cc(cnc1)Cl,-4.01 355 | CBr,-0.82 356 | CO,-5.1 357 | CCCCCCC=O,-2.67 358 | c1cc(c(c(c1)Cl)c2c(cccc2Cl)Cl)Cl,-2.28 359 | c1ccc(c(c1)N)[N+](=O)[O-],-7.37 360 | CN1CCCCC1,-3.88 361 | CCCCCCCC=O,-2.29 362 | c1ccc(cc1)[N+](=O)[O-],-4.12 363 | C[C@@H]1CC[C@H](C(=O)C1)C(C)C,-2.53 364 | C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O)O)O)O)O,-25.47 365 | CF,-0.22 366 | CS(=O)C,-9.280000000000001 367 | c1ccc2c(c1)Oc3ccccc3O2,-3.15 368 | Cc1ccccc1N,-5.53 369 | CCCCBr,-0.4 370 | CCCCCCCCCO,-3.88 371 | Cc1ccncc1,-4.93 372 | C(=C(Cl)Cl)(Cl)Cl,0.1 373 | CC(C)(C)Br,0.84 374 | C=C(c1ccccc1)c2ccccc2,-2.78 375 | CCc1ccc(cc1)C,-0.95 376 | Cc1cccnc1,-4.77 377 | COCC(OC)(OC)OC,-5.73 378 | c1ccc-2c(c1)Cc3c2cccc3,-3.35 379 | CC(=O)N,-9.71 380 | COS(=O)(=O)OC,-5.1 381 | C(C(Cl)Cl)(Cl)Cl,-2.37 382 | COC(=O)C1CCCCC1,-3.3 383 | CCCCCCBr,0.18 384 | CCCCCCCBr,0.34 385 | c1ccc2c(c1)Oc3cccc(c3O2)Cl,-3.52 386 | COC(CC#N)(OC)OC,-6.4 387 | CC[C@H](C)Cl,0 388 | CCCCCCc1ccccc1,-0.04 389 | COc1cc(c(c(c1O)OC)Cl)C=O,-7.78 390 | c1cc(cc(c1)C(F)(F)F)C(F)(F)F,1.07 391 | c1ccc(cc1)Cn2ccnc2,-7.63 392 | c1ccc2c(c1)cccc2N,-7.28 393 | CCOC(=O)CC(=O)OCC,-6 394 | CC(=O)C1CC1,-4.61 395 | c1cc[nH]c1,-4.78 396 | c1cc(c(cc1c2ccc(cc2F)F)C(=O)O)O,-9.4 397 | CC1CCC(CC1)C,2.11 398 | C1CCC(CC1)O,-5.46 399 | CN(C)CCC=C1c2ccccc2CCc3c1cccc3,-7.43 400 | c1cc(ccc1O)F,-6.19 401 | c1ccc(c(c1)N)Cl,-4.91 402 | Cc1ccc(c(c1)C)C,-0.86 403 | CCc1ccccc1C,-0.85 404 | C[C@@H]1CC[C@H](CC1=O)C(=C)C,-3.75 405 | c1ccc(cc1)c2ccccc2,-2.7 406 | Cc1cccc(c1C)O,-6.16 407 | COP(=S)(OC)Oc1ccc(cc1)[N+](=O)[O-],-7.19 408 | CCOP(=S)(OCC)Oc1ccc(cc1)[N+](=O)[O-],-6.74 409 | CCN(CC)c1c(cc(c(c1[N+](=O)[O-])N)C(F)(F)F)[N+](=O)[O-],-5.66 410 | CSC,-1.61 411 | C[C@@H](c1cccc(c1)C(=O)c2ccccc2)C(=O)O,-10.78 412 | C1CCC(C1)O,-5.49 413 | CCCCC(=O)OC,-2.56 414 | CCCC(=C)C,1.47 415 | C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O,-8.42 416 | CCCN(CCC)c1c(cc(cc1[N+](=O)[O-])S(=O)(=O)C)[N+](=O)[O-],-7.98 417 | C=CCl,-0.59 418 | Cc1ccc(cc1)C(=O)N(C)C,-9.76 419 | CCCC(=O)CCC,-2.92 420 | COC(=O)c1ccccc1,-3.92 421 | Cc1ccc(cc1)C=O,-4.27 422 | CCCC(=O)OCCC,-2.28 423 | C1CNCCN1,-7.4 424 | CCOP(=S)(OCC)S[C@@H](CCl)N1C(=O)c2ccccc2C1=O,-5.74 425 | CCOCCO,-6.69 426 | CCC(C)CC,2.51 427 | Cc1cnccn1,-5.51 428 | CCC[N+](=O)[O-],-3.34 429 | Cc1cc(cc(c1)C)C,-0.9 430 | c1c(c(=O)[nH]c(=O)[nH]1)F,-16.92 431 | CCO,-5 432 | Cc1ccc(c2c1cccc2)C,-2.82 433 | c1c2c(cc(c1Cl)Cl)Oc3cc(c(cc3O2)Cl)Cl,-3.37 434 | c1cc(c(c(c1)Cl)C#N)Cl,-4.71 435 | CCOC=O,-2.56 436 | c1c(c(cc(c1Cl)Cl)Cl)Cl,-1.34 437 | CCOC(OCC)Oc1ccccc1,-5.23 438 | c1cc(cc(c1)O)[N+](=O)[O-],-9.62 439 | CCCCCCCCO,-4.09 440 | CCC=C,1.38 441 | C(Cl)(Cl)(Cl)Cl,0.08 442 | c1ccc(cc1)CCO,-6.79 443 | CN(C)C(=O)Nc1ccccc1,-9.13 444 | CSSC,-1.83 445 | C1C=CC[C@@H]2[C@@H]1C(=O)N(C2=O)SC(Cl)(Cl)Cl,-9.01 446 | CC(=O)OCC(COC(=O)C)OC(=O)C,-8.84 447 | COC,-1.91 448 | CCCCCC,2.48 449 | C(CBr)Br,-2.33 450 | C(C(Cl)(Cl)Cl)(Cl)Cl,-1.23 451 | c1c(c(=O)[nH]c(=O)[nH]1)C(F)(F)F,-15.46 452 | Cc1cccc(c1N)C,-5.21 453 | CCCOC(=O)C,-2.79 454 | c1ccc2c(c1)cccn2,-5.72 455 | CCS,-1.14 456 | CCSSCC,-1.64 457 | c1ccsc1,-1.4 458 | CCc1cccc2c1cccc2,-2.4 459 | CCCC(=O)C,-3.52 460 | c1c(c(c(c(c1Cl)Cl)Cl)Cl)c2c(cc(c(c2Cl)Cl)Cl)Cl,-4.61 461 | CCC[N@@](CC1CC1)c2c(cc(cc2[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-],-2.45 462 | CC(=O)O,-6.69 463 | CC=O,-3.5 464 | c1cc(cc(c1)[N+](=O)[O-])N,-8.84 465 | CCCCC#C,0.29 466 | COc1ccccc1N,-6.12 467 | c1ccc(cc1)O,-6.6 468 | CCC#N,-3.84 469 | c1ccc2c(c1)cccc2O,-7.67 470 | CCCCOC(=O)C,-2.64 471 | CC(C)(/C=N\OC(=O)NC)SC,-9.84 472 | Cc1ccccc1O,-5.9 473 | CC(C)C=O,-2.86 474 | CCC(=O)N,-9.4 475 | CCCBr,-0.56 476 | CC(C)Cl,-0.25 477 | C(CCl)CCl,-1.89 478 | c1cc(ccc1[N+](=O)[O-])O,-10.64 479 | C[C@@H](CCl)Cl,-1.27 480 | c1cc(ccc1N)Cl,-5.9 481 | c1ccc2c(c1)C(=O)c3cccc(c3C2=O)N,-9.44 482 | Cc1cccnc1C,-4.82 483 | c1cnccc1C#N,-6.02 484 | CCOP(=S)(OCC)SCSCC,-4.37 485 | CC(=O)C1CCCCC1,-3.9 486 | Cc1ccccc1C=O,-3.93 487 | CC(=O)c1ccncc1,-7.62 488 | c1c2c(cc(c1Cl)Cl)Oc3c(c(c(c(c3Cl)Cl)Cl)Cl)O2,-3.71 489 | CC(=O)C,-3.8 490 | CC(=C)C,1.16 491 | c1cc(c(cc1Cl)c2cc(c(c(c2)Cl)Cl)Cl)Cl,-3.61 492 | CCCCC[N+](=O)[O-],-2.82 493 | CCC/C=C/C=O,-3.68 494 | CN(C)C(=O)c1ccc(cc1)[N+](=O)[O-],-11.95 495 | C1CCOC1,-3.47 496 | CCCCCCCC,2.88 497 | CCCN(CCC)c1c(cc(cc1[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-],-3.25 498 | CC(=CCC[C@](C)(C=C)OC(=O)C)C,-2.49 499 | C[C@@H](CCO[N+](=O)[O-])O[N+](=O)[O-],-4.29 500 | CC(C)OC(C)C,-0.53 501 | CCCCC(C)C,2.93 502 | c1(c(c(c(c(c1Cl)Cl)Cl)Cl)Cl)N(=O)=O,-5.22 503 | [C@@H](C(F)(F)F)(Cl)Br,-0.11 504 | CCCCOCCCC,-0.83 505 | CCCCCC1CCCC1,2.55 506 | CC(C)CC(C)C,2.83 507 | Cc1ccc(nc1)C,-4.72 508 | C/C=C/C=O,-4.22 509 | CCC[C@H](C)CC,2.71 510 | c1cc(c(c(c1)Cl)c2c(cc(cc2Cl)Cl)Cl)Cl,-1.96 511 | c1ccc(cc1)O[C@@H](C(F)F)F,-1.29 512 | COCCOC,-4.84 513 | CC[C@H](C)c1ccccc1,-0.45 514 | c1ccc(cc1)CCCO,-6.92 515 | CC[C@@H](C)c1cc(cc(c1O)[N+](=O)[O-])[N+](=O)[O-],-6.23 516 | COc1ccc(cc1)C(=O)OC,-5.33 517 | CCC(=O)Nc1ccc(c(c1)Cl)Cl,-7.78 518 | C[C@@H](c1ccc2cc(ccc2c1)OC)C(=O)O,-10.21 519 | C1(C(C(C1(F)F)(F)F)(F)F)(F)F,3.43 520 | CC(C)CCOC(=O)C,-2.21 521 | CCCCCCCl,0 522 | CC(C)CC(=O)C,-3.05 523 | CCCCCC=O,-2.81 524 | c1cc(cc(c1)Cl)N,-5.82 525 | C1COCCN1,-7.17 526 | CCOC(C)OCC,-3.28 527 | CCCC[N@](CC)c1c(cc(cc1[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-],-3.51 528 | CS,-1.2 529 | C1[C@@H]2[C@H](COS(=O)O1)[C@@]3(C(=C([C@]2(C3(Cl)Cl)Cl)Cl)Cl)Cl,-4.23 530 | CC(=O)c1ccc(cc1)OC,-4.4 531 | C=CCO,-5.03 532 | CCSC,-1.5 533 | CCCCCOC(=O)C,-2.51 534 | c1c(cc(c(c1Cl)Cl)Cl)Cl,-1.62 535 | CC(=O)c1ccccc1,-4.58 536 | CCCl,-0.63 537 | CCCC1CCCC1,2.13 538 | c1c(cc(cc1Cl)Cl)Cl,-0.78 539 | CCCOC(=O)c1ccc(cc1)O,-9.37 540 | c1cc(cc(c1)Cl)O,-6.62 541 | CC(C)CCO,-4.42 542 | CCCCCN,-4.09 543 | Cc1c(c(=O)n(c(=O)[nH]1)C(C)(C)C)Cl,-11.14 544 | CC(C)CCC(C)(C)C,2.93 545 | CCCCOCCO,-6.25 546 | C1[C@@H]2[C@H]3[C@@H]([C@H]1[C@H]4[C@@H]2O4)[C@@]5(C(=C([C@]3(C5(Cl)Cl)Cl)Cl)Cl)Cl,-4.82 547 | c1ccc(cc1)C(=O)N,-11 548 | CC(C)[N+](=O)[O-],-3.13 549 | C(C(CO)O)O,-13.43 550 | CCCI,-0.53 551 | COCCN,-6.55 552 | C(C(Cl)(Cl)Cl)Cl,-1.43 553 | CCC(=O)OC,-2.93 554 | C1CCCC1,1.2 555 | CCc1cccnc1,-4.59 556 | Cc1cc(cnc1)C,-4.84 557 | COCCO,-6.619999999999999 558 | COC=O,-2.78 559 | c1ccc2cc(ccc2c1)N,-7.47 560 | Cc1c[nH]cn1,-10.27 561 | Cc1cccc(c1)[N+](=O)[O-],-3.45 562 | C(CCCl)CCl,-2.32 563 | CC(=O)CO[N+](=O)[O-],-5.99 564 | CC(C)(C)c1ccccc1,-0.44 565 | CCCCCC(=O)OC,-2.49 566 | C[C@@H](C(F)(F)F)O,-4.16 567 | CCCCCBr,-0.1 568 | CCCCCCC=C,1.92 569 | CC1=CC(=O)[C@@H](CC1)C(C)C,-4.51 570 | CC(C)O,-4.74 571 | CCCCCCN,-3.95 572 | C(CO[N+](=O)[O-])CO[N+](=O)[O-],-4.8 573 | Cc1ccc(c(c1)C)O,-6.01 574 | CCCCCO,-4.57 575 | CCC[C@@H](C)O,-4.39 576 | CCCC[C@@H](C)CC,2.97 577 | C[C@@H](c1ccc(cc1)CC(C)C)C(=O)O,-7 578 | CCOC(=O)C[C@H](C(=O)OCC)SP(=S)(OC)OC,-8.15 579 | Cc1ccc(cc1C)O,-6.5 580 | Cc1cc(ccc1Cl)O,-6.79 581 | CCCC/C=C/C,1.68 582 | CCCOCCC,-1.16 583 | C[C@@H]1CC[C@H]([C@@H](C1)O)C(C)C,-3.2 584 | CCNc1nc(nc(n1)SC)NC(C)(C)C,-6.68 585 | CC(C)CC(C)(C)C,2.89 586 | CCCCC(=O)CCCC,-2.64 587 | CCCCN(CC)C(=O)SCCC,-3.64 588 | CCCCCC=C,1.66 589 | CC(C)OC=O,-2.02 590 | CC(OC(=O)C)OC(=O)C,-4.97 591 | c1c(c(=O)[nH]c(=O)[nH]1)Cl,-17.74 592 | CC(=C)c1ccccc1,-1.24 593 | CCC(C)C,2.38 594 | CCCCO[N+](=O)[O-],-2.09 595 | c1ccc(cc1)Br,-1.46 596 | CC(Cl)(Cl)Cl,-0.19 597 | CC(=C)[C@H]1CCC(=CC1)C=O,-4.09 598 | Cc1ccccc1[N+](=O)[O-],-3.58 599 | CCCCCCCI,0.27 600 | c1cc2ccc3cccc4c3c2c(c1)cc4,-4.52 601 | CCCCCCl,-0.1 602 | CC(C)COC(=O)C,-2.36 603 | CCC(C)(C)C,2.51 604 | c1cc(ccc1N)N(=O)=O,-9.82 605 | COC(=O)CC#N,-6.72 606 | COc1ccc(cc1)N,-7.48 607 | CC(C)Cc1ccccc1,0.16 608 | c1ccc(cc1)c2c(cc(cc2Cl)Cl)Cl,-2.16 609 | CN,-4.55 610 | c1ccc(c(c1)O)Cl,-4.55 611 | c1ccc2c(c1)C(=O)c3ccc(cc3C2=O)N,-11.53 612 | C(=C\Cl)\Cl,-1.17 613 | CCCCC(=O)C,-3.28 614 | C(CO[N+](=O)[O-])O[N+](=O)[O-],-5.73 615 | c1ccc(c(c1)O)F,-5.29 616 | Cc1c(nc(nc1OC(=O)N(C)C)N(C)C)C,-9.41 617 | C=Cc1ccccc1,-1.24 618 | CCOP(=O)(OCC)OCC,-7.5 619 | C(C(F)(F)F)O,-4.31 620 | CCCCOC[C@H](C)O,-5.73 621 | CCCO,-4.85 622 | Cc1ccccc1C,-0.9 623 | CC(C)(C)C,2.51 624 | CCCC#C,0.01 625 | c1ccc2c(c1)C(=O)NC2=O,-9.61 626 | CCCCI,-0.25 627 | Cc1ccc(cc1)O,-6.13 628 | CC(C)I,-0.46 629 | COc1ccccc1O,-5.94 630 | C1CC=CC1,0.56 631 | C[C@H](C(F)(F)F)O,-4.2 632 | CCCN,-4.39 633 | c1ccc(c(c1)[N+](=O)[O-])O,-4.58 634 | Cc1cccc2c1cccc2,-2.44 635 | c1(c(c(c(c(c1Cl)Cl)Cl)Cl)Cl)Cl,-2.33 636 | CCCCC/C=C/C=O,-3.43 637 | CCCCCCC#C,0.71 638 | CCOP(=S)(OCC)Oc1cc(nc(n1)C(C)C)C,-6.48 639 | CCCCCCCC(=O)OC,-2.04 640 | C1CCNC1,-5.48 641 | c1cc(ccc1C=O)O,-8.83 642 | CCCCCCCCl,0.29 643 | C1COCCO1,-5.06 644 | -------------------------------------------------------------------------------- /Projects/Projects-Fall-2021/Data/pdbbind_core.csv: -------------------------------------------------------------------------------- 1 | smiles,-logKd/Ki 2 | Cn1nc(-c2ccc3c(c2)OCO3)cc1N,2.0699999999999998 3 | [NH3+]CCc1ccccc1,2.27 4 | O=C1NC2C(O)C(O)C(O)C(CO)N2C1=O,2.2799999999999998 5 | OCC1OC(OC2C(CO)OC(O)C(O)C2O)C(O)C(O)C1O,2.3500000000000001 6 | O=C1C=C(Nc2nnn[nH]2)CCC1,2.3599999999999999 7 | N=c1ccn(C2OC(CO)C(O)C(F)C2O)c(=O)[nH]1,2.3999999999999999 8 | Nc1ncc(CCC([NH3+])C(=O)[O-])[nH]1,2.52 9 | CC(C)CC([NH3+])C(=O)[O-],2.5499999999999998 10 | [NH3+]Cc1ccc(C(=O)[O-])cc1,2.5699999999999998 11 | CC(=O)Oc1c(C)cccc1C(=O)[O-],2.77 12 | COc1ccc(S(N)(=O)=O)cc1,2.8199999999999998 13 | O=C1Nc2ccccc2C12CC[NH2+]C2,2.8500000000000001 14 | CCOC(=O)C=Cc1ccc(O)c(O)c1,2.8900000000000001 15 | COc1ccc2[nH]ccc2c1,2.96 16 | CCN(CC)C(=O)c1ccc(O)c(OC)c1,3.1000000000000001 17 | [NH3+]CCc1c[nH]c2ccc(O)cc12,3.1600000000000001 18 | [NH3+]C(CCC(=O)NC(CS)C(=O)NCC(=O)[O-])C(=O)[O-],3.1800000000000002 19 | Nc1ccc2cc3ccc(N)cc3nc2c1,3.2799999999999998 20 | C=CC[NH+](Cc1ccccc1C(=O)NCc1ccccc1)Cc1ccc2c(c1C(=O)[O-])OCO2,3.2799999999999998 21 | CC(=O)NC1C(O)C=C(C(=O)[O-])OC1C(O)C(O)C[NH3+],3.3999999999999999 22 | Nc1nc(CCc2ccccc2)cc(=O)[nH]1,3.6600000000000001 23 | O=C([O-])C1(O)C=CC(O)C(O)C1,3.7000000000000002 24 | C=CCOC1(C(=O)[O-])CC(OC2(C(=O)[O-])OC(C(O)CO)C(O)C(O)C2O)C(O)C(C(O)CO)O1,3.7200000000000002 25 | CCCCCCCCCC(=O)[O-],3.7799999999999998 26 | O=C([O-])COc1c(Br)csc1C(=O)[O-],3.7999999999999998 27 | CC(=O)NC1C(O)C[NH2+]C(CO)C(O)C1O,4.0499999999999998 28 | CCCCc1nc2c(=O)[nH][nH]c(=O)c2[nH]1,4.0800000000000001 29 | [NH3+]CCC[NH2+]CCCC[NH2+]CCC[NH3+],4.0800000000000001 30 | O=C(NC1OC(CO)C(O)C(O)C1O)c1ccccc1,4.0899999999999999 31 | C[NH+](C)Cc1cccc(C(=O)Nc2cccc(-c3nn[nH]n3)c2)c1,4.1200000000000001 32 | O=C(NCC=CC1OC(n2ccc(=O)cc2)C(O)C1O)c1cc([N+](=O)[O-])cc(O)c1O,4.1299999999999999 33 | CC(C)CC([NH3+])C(=O)[O-],4.1900000000000004 34 | Nc1cccc2c(S(=O)(=O)[O-])cccc12,4.1900000000000004 35 | [NH3+]C(Cc1c[nH]c2ccccc12)C(=O)[O-],4.1900000000000004 36 | CC1OC(OC2C(CO)OC(OC3C(CO)OC(O)C(O)C3O)C(O)C2O)C(O)C(O)C1[NH2+]C1C=C(CO)C(O)C(O)C1O,4.21 37 | [NH3+]C(CCCC[N+](=O)[O-])C(=O)[O-],4.2199999999999998 38 | CC(C)CC(NC(=O)C(CC(=O)[O-])NC(=O)C([NH3+])CCC(=O)[O-])C(=O)[O-],4.2999999999999998 39 | OCC1OC(OC2C(O)C[NH2+]OC2CO)C(O)C(O)C1O,4.2999999999999998 40 | COc1ccc2c(c1)cc(CNS(=O)(=O)c1cc3ccccc3o1)n2CC(=O)[O-],4.2999999999999998 41 | COC1OC(CO)C(O)C(O)C1O,4.3700000000000001 42 | CCCCCCCCCC(=O)[O-],4.3799999999999999 43 | O=S(=O)([O-])c1cccc2cccc(Nc3ccccc3)c12,4.4299999999999997 44 | CCCCCCCCN=C1OCC2C(O)C(O)C(O)C(O)N12,4.4500000000000002 45 | COC1C(CO)OC(OC2C(CO)OC(OC3C(CO)NC(=NO)C(O)C3O)C(O)C2O)C(O)C1O,4.5999999999999996 46 | Cn1c(=O)c2c(ncn2CC(O)CO)n(C)c1=O,4.5999999999999996 47 | CNC(=O)C([NH3+])Cc1ccc(OCc2ccccc2)cc1,4.6600000000000001 48 | CC([NH3+])C1CCC(C(=O)Nc2ccncc2)CC1,4.7599999999999998 49 | OC1(c2ccc(-c3ccccc3)cc2)C[NH+]2CCC1CC2,4.7599999999999998 50 | CC=COC1(C(=O)[O-])CC(O)C(O)C(C(O)COC2(C(=O)[O-])C=CCC(C(O)CO)O2)O1,4.7999999999999998 51 | CC1CC(C)CN(C(=O)c2cc(Br)ccc2N)C1,4.8499999999999996 52 | OCCNc1ncnc2oc(-c3ccccc3)c(-c3ccccc3)c12,4.8600000000000003 53 | Cc1o[nH]c(=O)c1CC([NH3+])C(=O)[O-],4.8899999999999997 54 | OCC1CC2C(O)C(O)C1(O)C[NH+]2Cc1ccccc1,4.8899999999999997 55 | OCC1C(O)C(O)C(O)c2nccn21,4.8899999999999997 56 | C=C(CC)C(=O)c1ccc(OCC(=O)[O-])c(Cl)c1Cl,4.9400000000000004 57 | COC1=C2CC(C)CC(OC)C(O)C(C)C=C(C)C(OC(N)=O)C(OC)C=CC=C(C)C(=O)NC(=CC1=O)C2=O,5.0599999999999996 58 | NC(=[NH2+])c1ccc(N)cc1,5.0999999999999996 59 | C=CC[NH+](Cc1ccccc1C(=O)NC(c1ccc(OC)cc1)c1ccc(OC)cc1)Cc1ccc2c(c1C(=O)[O-])OCO2,5.1200000000000001 60 | CC(C)C([NH3+])C(=O)N1CCCC1C(=O)NCC(=O)NC(CO)C(=O)NC(CCC(N)=O)C(=O)NC(Cc1c[nH]cn1)C(=O)NC(Cc1ccc(O)cc1)C(=O)NC(CC(=O)[O-])C(=O)NC(CO)C(=O)[O-],5.2199999999999998 61 | CC(C)C1[NH2+]CC(O)C(O)C1O,5.2199999999999998 62 | CCCCCCCCCCCC(=O)[O-],5.2400000000000002 63 | CC=C1C2C=C(C)CC1([NH3+])c1ccc(=O)[nH]c1C2,5.3700000000000001 64 | CC(=O)NC1C(O)C=C(C(=O)[O-])OC1C(O)C(O)CO,5.4000000000000004 65 | COC1OC(C)C(O)C(O)C1O,5.4000000000000004 66 | CCC1CC1(NC(=O)C1CC2CN1C(=O)C(C(C)(C)C)NC(=O)OCC(C)(C)CCCCc1cccc3cn(cc13)C(=O)O2)C(=O)NS(=O)(=O)C1CC1,5.5800000000000001 67 | CCCC(=O)NC1C(=NOC(=O)Nc2ccccc2)OC(CO)C(O)C1O,5.5999999999999996 68 | O=C(CCC1=CC(O)(C(=O)[O-])CC(O)C1O)Nc1ccccc1,5.6399999999999997 69 | CC(C)C([NH3+])C(=O)N1CCCC1,5.7000000000000002 70 | NC(=[NH2+])c1cc2c(I)cccc2s1,5.7699999999999996 71 | C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1coc(-c3ccc(Cl)s3)n1)[NH+]1CCCC21,5.79 72 | COc1ccc(S(=O)(=O)NC(C)C(=O)[O-])cc1,5.8499999999999996 73 | O=c1cc(-c2ccccc2)oc2cc(O)cc(O)c12,5.8499999999999996 74 | C[NH+]1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1,5.9100000000000001 75 | Cc1cncc2cccc(S(=O)(=O)N3CCC[NH2+]CC3C)c12,5.96 76 | O=C(Nc1cccc(-c2nnn[nH]2)c1)c1ccc2n[nH]cc2c1,5.96 77 | C=CC(=O)Nc1ccc2c(Nc3ccn[nH]3)nc(-c3ccccc3)nc2c1,5.9900000000000002 78 | OCC[NH+]1CC(O)C(O)C(O)C1CO,6.0 79 | OCC1C[NH2+]C(O)C(O)C1O,6.0099999999999998 80 | CC(C)(C)C([NH3+])C(=O)NS(=O)(=O)OCC1OC(n2cnc3c(N)ncnc32)C(O)C1O,6.0199999999999996 81 | [NH3+]C(Cc1ccc(F)cc1)C(=O)[O-],6.0199999999999996 82 | Cc1c2cc[nH]c(=O)c2c(C)c2c1[nH]c1ccc(O)cc12,6.0700000000000003 83 | O=C(CCC(O)(O)C(Cc1ccccc1)NC(=O)c1ccccc1)NC(Cc1ccccc1)C(=O)[O-],6.0800000000000001 84 | CCc1cc(-c2n[nH]c(C(=O)[O-])c2-c2ccc3c(c2)OCO3)c(O)cc1O,6.1699999999999999 85 | NS(=O)(=O)c1ccc2c(c1)C[NH2+]CC2,6.2400000000000002 86 | Oc1cccc2cccnc12,6.2400000000000002 87 | [NH3+]C(Cc1ns[nH]c1=O)C(=O)[O-],6.2699999999999996 88 | OCC1OC(OC2C(CO)OC(OC3C(O)C[NH2+]OC3CO)C(O)C2O)C(O)C(O)C1O,6.2800000000000002 89 | NC(=O)c1cccc(-c2cc(Nc3ccc(OC(F)(F)F)cc3)ncn2)c1,6.2999999999999998 90 | O=C([O-])COc1c(C(=O)[O-])sc(-c2cccc(NCc3ccccc3)c2)c1Br,6.3300000000000001 91 | [NH3+]C(CCC(=O)NC(CSCc1ccccc1)C(=O)NC(C(=O)[O-])c1ccccc1)C(=O)[O-],6.4000000000000004 92 | OCC1NC(=NO)C(O)C(O)C1O,6.4199999999999999 93 | Nc1ccc2nc(N)[nH]c(=O)c2c1,6.46 94 | NS(=O)(=O)c1ccccc1F,6.5 95 | O=C([O-])c1ccc(Nc2nccc(Nc3ccccc3-c3ccccc3)n2)cc1,6.5199999999999996 96 | CC(C)(C)c1ccc(C(=O)NNC(=O)Nc2cccc3ccccc23)cc1[N+](=O)[O-],6.54 97 | COc1cc(CCc2ccccc2)c(C(=O)[O-])c(O)c1CC=C(C)C,6.6299999999999999 98 | CC([NH2+]C(CCc1ccccc1)C(=O)NC(CCCNC(N)=[NH2+])C(=O)Nc1ccccc1)C(=O)[O-],6.6399999999999997 99 | O=C1CCCCCC=CCCOC(=O)c2c(O)cc(O)c(Cl)c2C1,6.6799999999999997 100 | COC1OC(C)C(O)C(O)C1O,6.7199999999999998 101 | COc1cccc(-c2cccc(C3(c4ccccc4)NC(=[NH2+])N(C)C3=O)c2)c1,6.7199999999999998 102 | Cc1c(-c2ccnc(Nc3ccc(N4CC[NH2+]CC4)cc3)n2)sc(=O)n1C,6.8300000000000001 103 | O=C(NCC=CC1OC(Sc2ccncc2)C(O)C1O)c1cc([N+](=O)[O-])cc(O)c1O,6.8799999999999999 104 | [NH3+]C(CC1(C(=O)[O-])CC2OCCCC2O1)C(=O)[O-],6.9000000000000004 105 | Cc1ccccc1S(=O)(=O)Nc1cc(-c2ccc(C#N)cc2)sc1C(=O)[O-],6.9199999999999999 106 | CCCCCCCCCCCCCCCCCC(=O)[O-],6.9199999999999999 107 | O=c1cc(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12,7.0 108 | CC1OC(OC2C(CO)OC(O)C(O)C2O)C(O)C(O)C1[NH2+]C1CC(CO)C(OC2OC(CO)C(OC3OC(CO)C(O)C(O)C3O)C(O)C2O)C(O)C1O,7.1200000000000001 109 | CC(O)(CS(=O)(=O)c1ccc(F)cc1)C(=O)Nc1ccc(C#N)c(C(F)(F)F)c1,7.1200000000000001 110 | OCC1C(O)C(O)C(O)c2nc(CNc3ccccc3)cn21,7.1399999999999997 111 | NC(=[NH2+])c1ccc(C2C3C(=O)N(Cc4ccc(F)cc4)C(=O)C3C3CCC[NH+]32)cc1,7.2400000000000002 112 | [NH3+]C(CC(=O)N1CCCCCN1C(=O)c1ccccc1)Cc1cc(F)c(F)cc1F,7.25 113 | CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1,7.25 114 | CCNC1=NC2C(OC(CO)C(O)C2O)S1,7.2999999999999998 115 | NC(=NO)NCCC([NH3+])C(=O)[O-],7.3200000000000003 116 | CC(C)(C)NC(=O)C1CC2CCCCC2C[NH+]1CC(O)C(Cc1ccccc1)NC(=O)C(CC(N)=O)NC(=O)c1ccc2ccccc2n1,7.3200000000000003 117 | CC(C)(C)OC(=O)NC1CCCCCC=CC2CC2(C(=O)NS(=O)(=O)C2CC2)NC(=O)C2CC(OC(=O)n3cc4cccc(F)c4c3)CN2C1=O,7.3499999999999996 118 | CC(=O)NC1C([NH3+])C=C(C(=O)[O-])OC1C(O)C(O)CO,7.4000000000000004 119 | OC1CC2OCC=C3C[NH+]4CCC56c7ccccc7N1C5C2C3CC46,7.4199999999999999 120 | OCC(O)C(O)C(O)C(O)C(O)C[S+]1CC(O)C(O)C1CO,7.5199999999999996 121 | COc1ccc(CC2C(=O)C(O)C(O)CC2(O)C(=O)[O-])cc1,7.5899999999999999 122 | NC(=[NH2+])NCc1ccccc1CCCCCCCCCC(=O)NC(CCCC[NH3+])C(=O)NO,7.7000000000000002 123 | O=c1oc2c(O)c(O)cc3c(=O)oc4c(O)c(O)cc1c4c23,7.7000000000000002 124 | CC(C)CC([NH3+])C(=O)[O-],7.7000000000000002 125 | O=[N+]([O-])c1ccc2c(c1)C[NH2+]C(CO)C2,7.7699999999999996 126 | CC1[NH2+]C(C[NH3+])C(O)C(O)C1O,7.79 127 | [NH3+]C(C(=O)[O-])C1CCC1C(=O)[O-],7.8899999999999997 128 | NC(=[NH2+])c1ccc2[nH]c(-c3cccc(-c4ccccc4)c3O)cc2c1,7.96 129 | CC(CS)C(=O)N1CCCC1C(=O)[O-],7.96 130 | CC(C)(C)NC(=O)C1CC2CCCCC2C[NH+]1CC(O)C(Cc1ccccc1)NC(=O)C(CC(N)=O)NC(=O)c1ccc2ccccc2n1,8.0 131 | OCC1C(O)C(O)C(O)c2nc(CCc3ccccc3)cn21,8.0199999999999996 132 | NC(=[NH2+])NCCCC(NC(=O)C(CCCNC(N)=[NH2+])NC(=O)CCCCCNC(=O)C(CCCC[NH3+])NC(=O)CCCCCNC(=O)C1OC(n2cnc3c(N)ncnc32)C(O)C1O)C(N)=O,8.0500000000000007 133 | [NH3+]CCCCC([NH2+]C(CCc1ccccc1)C(=O)[O-])C(=O)NC(Cc1c[nH]c2ccccc12)C(=O)[O-],8.1799999999999997 134 | C[NH2+]C1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4O,8.25 135 | CC(=O)NC1C(=NOC(=O)Nc2ccccc2)OC(CO)C(O)C1O,8.2699999999999996 136 | COC(C(=O)N1Cc2[nH]nc(NC(=O)c3ccc(N4CC[NH+](C)CC4)cc3)c2C1)c1ccccc1,8.3000000000000007 137 | O=c1ccc2c([nH]1)CCCC2[NH2+]CCCCCCCCCCCC[NH2+]C1CCCc2[nH]c(=O)ccc21,8.3499999999999996 138 | O=C([O-])COc1c(C(=O)[O-])sc(-c2cccc(NC3CCN(S(=O)(=O)Cc4ccccc4)CC3)c2)c1Br,8.4000000000000004 139 | CC(=O)Nc1ccc(OCC(C)(O)C(=O)Nc2ccc([N+](=O)[O-])c(C(F)(F)F)c2)cc1,8.4000000000000004 140 | O=C(Nc1ccc(Cl)cn1)C1C[NH+](CC(F)F)CC1C(=O)Nc1ccc(-n2ccccc2=O)cc1F,8.4299999999999997 141 | COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N,8.5199999999999996 142 | COC1CC(C)Cc2cc(O)cc(c2)NC(=O)C(C)=CCCC(C)C(OC(N)=O)C(C)=CC(C)C1O,8.5199999999999996 143 | CC(C)(C)c1ccc(C(=O)CC2CCC(O)C3C(O)C(O)C[NH+]23)cc1,8.5700000000000003 144 | O=NC(=O)CNS(=O)(=O)c1ccc(-c2ccccc2)cc1,8.6300000000000008 145 | Cc1nc2nc(C(=O)N3CCOCC3)cn2c(-c2ccc(Cl)cc2Cl)c1C[NH3+],8.6600000000000001 146 | CC(C)CC(NC(=O)C(CCc1ccc(-c2ccc(F)cc2)cc1)CC(CCCCN1Cc2ccccc2C1=O)C(=O)[O-])C(=O)Nc1ccccc1,8.6999999999999993 147 | c1ccc(C2([NH+]3CCCCC3)CCCCC2)cc1,8.6999999999999993 148 | CNc1nc2c(CC[NH2+]CC3CCCC3)c3[nH]c(=[NH2+])[nH]c(=O)c3cc2[nH]1,8.6999999999999993 149 | O=C([O-])c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1,8.7400000000000002 150 | O=S(=O)(Nc1ccc(Cl)cc1)c1ccc2c(c1)C[NH2+]C(CO)C2,8.8499999999999996 151 | Cc1ccc(C(=O)Nc2cccc(N3CCOCC3)c2)cc1-c1ccc2c(C3CC[NH2+]CC3)noc2c1,8.8499999999999996 152 | COc1ccc2c(-c3c(C)n(Cc4cc(OC(C)C(=O)[O-])ccc4Cl)c4cc(OC(F)(F)F)ccc34)noc2c1,9.0 153 | CCOc1ccc2nc(S(N)(=O)=O)sc2c1,9.0 154 | Cc1ncnc2c1ncn2C1OC(C=CCNC(=O)c2cc(-c3ccc(F)cc3)cc(O)c2O)C(O)C1O,9.0 155 | CSCCC([NH2+]CC(Cc1ccccc1)NC(=O)C(CO)NC(=O)C(CC(C)C)NC(=O)C(Cc1cnc[nH]1)NC(=O)C1CCC[NH2+]1)C(=O)NC(C)C(=O)NC(Cc1cnc[nH]1)C(=O)[O-],9.0 156 | Cc1cccc(C(=O)N2c3cccc(O)c3NC3=C(C2c2ccc(OCc4ccccc4)cc2F)S(=O)(=O)CC(C)(C)C3)n1,9.0999999999999996 157 | CCC1CC1(NC(=O)C1CC2CN1C(=O)C(C(C)(C)C)NC(=O)OCC(C)(C)CCCCc1cccc3cn(cc13)C(=O)O2)C(=O)NS(=O)(=O)C1CC1,9.1300000000000008 158 | NC(=[NH2+])c1ccc2cc(C(=O)Nc3ccc(C[NH3+])cc3)cc(Nc3ncccn3)c2c1,9.2100000000000009 159 | C[NH2+]C1C(O)COC2CC(CC([NH3+])C(=O)[O-])(C(=O)[O-])OC21,9.3000000000000007 160 | O=C(NC1c2ccccc2-c2c(-c3nc4ccncc4[nH]3)cccc21)c1ccnc2[nH]ccc12,9.4600000000000009 161 | Cc1c(N=C2OC(C(F)(F)F)C3C(O)CCN23)ccc(C#N)c1Cl,9.5199999999999996 162 | C#Cc1cccc(Nc2nc3cc(C(=O)[O-])ccc3c3cncnc23)c1,9.7599999999999998 163 | CCC1=CC2Cc3nc4cc(Cl)ccc4c(N)c3C(C1)C2,9.8900000000000006 164 | CC1OC(OC2CCC3(C)C(CCC4C3CC(O)C3(C)C(C5=CC(=O)OC5)CCC43O)C2)CC(O)C1O,10.0 165 | CC1[NH2+]C(CNC(=O)Cc2c[nH]c3ccccc23)C(O)C(O)C1O,10.6 166 | CC[NH+](Cc1cc(Nc2nc(C)cn3c(-c4cn[nH]c4)cnc23)sn1)C(C)(C)CO,10.699999999999999 167 | CC(C)CNC(=O)C([NH2+]CC(Cc1ccccc1)NC(=O)c1cc(C(=O)NC(C)c2ccccc2)cc(N(C)S(C)(=O)=O)c1)C(C)O,10.77 168 | COc1ccc(S(=O)(=O)NC(CC(=O)NCc2ccc(C#N)cc2)C(=O)N2CCCC2C(=O)NCc2ccc(C(N)=[NH2+])cc2)cc1Cl,10.92 169 | COc1cc(Cl)cc(C(=O)Nc2ccc(Cl)cn2)c1NC(=O)c1scc(CN(C)C2=NCCO2)c1Cl,11.15 170 | -------------------------------------------------------------------------------- /Projects/Projects-Fall-2021/Readme.md: -------------------------------------------------------------------------------- 1 | # Projects of Students 2 | 3 | Group 1: 4 | -------------------------------------------------------------------------------- /Projects/README.md: -------------------------------------------------------------------------------- 1 | # Course Project Resources: 2 | To bring together and apply the various topics covered in this course, you will work on a machine learning project. The goal of the project is to go through the complete knowledge discovery process to answer one or more questions you have about a topic of your own choosing. You will acquire the data, formulate a question (or questions) of interest, perform the data analysis, and communicate the results. Projects are programming assignments that cover the topic of this course. Any project is written by **[Jupyter Notebook](http://jupyter.org)**. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Furthermore, we can include **mathematical notation** within markdown cells of Jupyter Notebook using **LaTeX**. 3 | 4 | ## َUploading and Presentation of Project 5 | ### Uploading Project in GitHub 6 | Students individually implement given assignments in recommended language. Each Student should create his/her own GitHub repository for his/her project. The repository should comprise a Readme.md file containing the analysis of the result. It should include a short write-up including the following components: 7 | 8 | - Problem statement and hypothesis 9 | - Description of your data set and how it was obtained 10 | - Description of any pre-processing steps you took 11 | - What you learned from exploring the data, including visualizations 12 | - How you chose which features to use in your analysis 13 | - Details of your modeling process, including how you selected your models and validated them 14 | - Your challenges and successes 15 | - Possible extensions or business applications of your project 16 | - Conclusions and key learnings 17 | 18 | ### Final Project 19 | Your project repository on GitHub should contain the following: 20 | 21 | - Project paper: Any format (PDF, Markdown, etc.) 22 | - Presentation slides: Any format (PDF, PowerPoint, Google Slides, IPython Notebook, etc.) 23 | - Code: Commented Jupyter Notebooks, and any other code you used in the project 24 | - Visualizations: Integrated into your paper and/or slides 25 | - Data: Data files in "raw" or "processed" format 26 | - Data dictionary (aka "code book"): Description of each variable, including units 27 | 28 | ### Project Presentation in the Class 29 | Each student will explain his/her project in a 10–15 minute presentation to the class. Presentations should clearly convey the project ideas, methods, and results, including the question(s) being addressed, the motivation of the analyses being employed, and relevant evaluations, contributions, and discussion questions. 30 | 31 | ## Coding: 32 | Programming assignments will require the use of Python 3.7, as well as additional Python packages as follows.
33 | * [Python 3.7:](https://www.python.org/downloads/) An interactive, object-oriented, extensible programming language. 34 | * [NumPy:](http://www.numpy.org) A Python package for scientific computing. 35 | * [Pandas:](https://pandas.pydata.org) A Python package for high-performance, easy-to-use data structures and data analysis tools. 36 | * [Scikit-Learn:](https://scikit-learn.org/stable/) A Python package for machine learning. 37 | * [Matplotlib:](https://matplotlib.org) A Python package for 2D plotting. 38 | * [SciPy:](https://www.scipy.org) A Python package for mathematics, science, and engineering. 39 | * [IPython:](https://ipython.org) An architecture for interactive computing with Python. 40 | 41 | Most of the relevant software is a part of the [SciPy stack](https://www.scipy.org), a collection of Python-based open source software for mathematics, science, and engineering (which includes Python, NumPy, the SciPy library, Matplotlib, pandas, IPython, and scikit-learn). The [Anaconda](https://www.anaconda.com) Python Distribution is a free distribution for the SciPy stack that supports Linux, Mac, and Windows. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python and R data science and machine learning on Linux, Windows, and Mac OS X. It's the industry standard for developing, testing, and training on a single machine.
42 | 43 | * [Getting started with conda](https://conda.io/docs/user-guide/getting-started.html) 44 | * [Instalation](https://docs.anaconda.com/anaconda/install/) 45 | 46 | Tutorial:
47 | * [NumPy Tutorial](http://scipy.github.io/old-wiki/pages/Tentative_NumPy_Tutorial)
48 | You can learn Python via the following websites:
49 | * [SoloLearn](http://www.sololearn.com/) (A great website for getting started with coding. It offers easy to follow lessons, interspersed with quizzes to help you retain what you are learning). 50 | * [Google Developer Python Tutorial](https://developers.google.com/edu/python/) (highly recommended as a way to master python in just a few hours!) 51 | 52 | ### Latex 53 | The students can include mathematical notation within markdown cells using LaTeX in their **[Jupyter Notebooks](http://jupyter.org)**.
54 | A Brief Introduction to LaTeX [PDF](https://www.seas.upenn.edu/~cis519/spring2018/assets/resources/latex/latex.pdf)
55 | Math in LaTeX [PDF](https://www.seas.upenn.edu/~cis519/spring2018/assets/resources/latex/math.pdf)
56 | Sample Document [PDF](https://www.seas.upenn.edu/~cis519/spring2018/assets/resources/latex/sample.pdf)
57 | 58 | Competitions 59 | 60 | ## Competitions: 61 | Here are some machine learning and data mining competition platforms: 62 | - [Kaggle](https://www.kaggle.com/) 63 | - [DrivenData](https://www.drivendata.org/) 64 | - [Analytics Vidhya](http://datahack.analyticsvidhya.com/) 65 | - [The Data Science Game](http://www.datasciencegame.com/) 66 | - [InnoCentive](https://www.innocentive.com/) 67 | - [TuneedIT](http://tunedit.org/challenges) 68 | -------------------------------------------------------------------------------- /Recitation-Assignments/Assignment-Set-1_Sample.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Assignment Set 1 by [Your_Name] for [Applied Machine Learning/Deep Learning] Course at Data Science Center, SBU\n" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [ 15 | { 16 | "name": "stdout", 17 | "output_type": "stream", 18 | "text": [ 19 | "yty-desktop\n", 20 | "yty\n" 21 | ] 22 | } 23 | ], 24 | "source": [ 25 | "!hostname\n", 26 | "!whoami" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 2, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "import sys\n", 36 | "from datetime import datetime\n", 37 | "import time\n", 38 | "import numpy as np\n", 39 | "import pandas as pd\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "import random\n", 42 | "import sklearn" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 3, 48 | "metadata": {}, 49 | "outputs": [ 50 | { 51 | "name": "stdout", 52 | "output_type": "stream", 53 | "text": [ 54 | "2021-11-13 13:25:11.805038 +0330 UTC\n" 55 | ] 56 | } 57 | ], 58 | "source": [ 59 | "print(datetime.now(), time.localtime().tm_zone, 'UTC')" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 4, 65 | "metadata": {}, 66 | "outputs": [ 67 | { 68 | "data": { 69 | "text/plain": [ 70 | "'3.7.4 (default, Aug 13 2019, 20:35:49) \\n[GCC 7.3.0]'" 71 | ] 72 | }, 73 | "execution_count": 4, 74 | "metadata": {}, 75 | "output_type": "execute_result" 76 | } 77 | ], 78 | "source": [ 79 | "sys.version" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 5, 85 | "metadata": {}, 86 | "outputs": [ 87 | { 88 | "data": { 89 | "image/png": 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", 90 | "text/plain": [ 91 | "

" 92 | ] 93 | }, 94 | "metadata": { 95 | "needs_background": "light" 96 | }, 97 | "output_type": "display_data" 98 | }, 99 | { 100 | "name": "stdout", 101 | "output_type": "stream", 102 | "text": [ 103 | " 0 1 2 3\n", 104 | "0 -0.586722 -0.207000 -1.308758 -0.152165\n", 105 | "1 1.272038 0.636308 -1.372132 1.016271\n", 106 | "2 -0.823139 1.236532 1.577816 -0.646164\n", 107 | "3 1.123740 -0.419440 -0.106504 -0.279846\n", 108 | "4 -0.117574 2.853033 -0.921138 0.908544\n", 109 | "5 -1.191960 1.204705 1.950481 0.557829\n", 110 | "6 -1.237749 0.478574 0.637579 0.850902\n", 111 | "7 0.913900 0.378192 1.484584 1.380534\n", 112 | "8 0.611769 0.156439 -0.678460 0.815976\n", 113 | "9 0.120389 0.706875 -0.713905 -0.130221\n", 114 | "\n", 115 | "-> Execution time was 11.725935935974121 seconds <-\n" 116 | ] 117 | } 118 | ], 119 | "source": [ 120 | "start_time = time.time()\n", 121 | "data = {'a': np.arange(500),\n", 122 | " 'c': np.random.randint(0, 500, 500),\n", 123 | " 'd': 0.3*np.random.randn(500)}\n", 124 | "data['b'] = data['a'] + 10 * np.random.randn(500)\n", 125 | "data['d'] = np.abs(data['d']) * 100\n", 126 | "\n", 127 | "plt.scatter('a', 'b', c='c', s='d', data=data)\n", 128 | "plt.xlabel('entry a')\n", 129 | "plt.ylabel('entry b')\n", 130 | "plt.show()\n", 131 | "df = pd.DataFrame(np.random.randn(10, 4)) \n", 132 | "print(df)\n", 133 | "\n", 134 | "j=1.23\n", 135 | "for i in range(80000000):\n", 136 | " j+=j**0.8157678\n", 137 | "\n", 138 | "print(\"\\n-> Execution time was %s seconds <-\" % (time.time() - start_time))\n" 139 | ] 140 | } 141 | ], 142 | "metadata": { 143 | "kernelspec": { 144 | "display_name": "Python 3", 145 | "language": "python", 146 | "name": "python3" 147 | }, 148 | "language_info": { 149 | "codemirror_mode": { 150 | "name": "ipython", 151 | "version": 3 152 | }, 153 | "file_extension": ".py", 154 | "mimetype": "text/x-python", 155 | "name": "python", 156 | "nbconvert_exporter": "python", 157 | "pygments_lexer": "ipython3", 158 | "version": "3.7.4" 159 | } 160 | }, 161 | "nbformat": 4, 162 | "nbformat_minor": 4 163 | } 164 | -------------------------------------------------------------------------------- /Recitation-Assignments/README2020.md: -------------------------------------------------------------------------------- 1 | # Teaching Assistants (Spring 2020) 2 | 3 | * (Head) [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) (Email: y.taheriyeganeh@mail.sbu.ac.ir) 4 | - Office Hours: Sundays and Wednesdays, 12 am to 1 pm, Professor's Office (TBC) 5 | * [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) (Email: r.a.erfan@gmail.com) 6 | 7 | * [Mostafa Khodayari](https://github.com/MSTF4) (Email: mo.khodayari@mail.sbu.ac.ir) 8 | 9 | * [Esmail Mafakheri](https://github.com/E008001) (Email: e.mafakheri@mail.sbu.ac.ir) 10 | 11 | **Please Note**: 12 | 13 | * A carbon copy (Cc) of your email communications with TAs must be sent to the following email address (hhhaji@yahoo.com). 14 | 15 | * Response to emails may take a few days. Please be patient! 16 | 17 | # Recitation 18 | 19 | * **Video Tutorial One** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) posted on Monday, Esfand 5 20 | - Brief on Working in Cloud-based Services, such as Google Colab 21 | - Brief Introduction of GIT 22 | - Video: Posted in the Skype Group 23 | 24 | * **Session One** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Wednesday, Esfand 7 25 | - Introduction to Python 26 | 27 | * **Session Two** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Wednesday, Esfand 7 28 | - Python Libraries Tutorial 29 | - Brief Introduction to Python Classes/Objects (Added to the Notebook!) 30 | - Notebook: [Colab](https://colab.research.google.com/drive/1pIxeznCzX16uI_ONooD644G2kwTrAFUJ) 31 | 32 | * **Session Three** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Esfand 13 33 | - The Machine Learning Landscape of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 34 | - Machine Learning with Scikit-Learn Part 1 35 | - Notebook: [Colab](https://colab.research.google.com/drive/1e_5IjXWMeJ0pq2UXneKCaT6GVfczJAoN) 36 | 37 | * **Session Four** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Esfand 20 38 | - Reviewing Session Three 39 | - End to End Machine Learning Project of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 40 | - Machine Learning with Scikit-Learn Part 2 41 | - Notebook: [Colab](https://colab.research.google.com/drive/15pmReFGAfmULTQb6RyZur9n0NCX6n8Tl) 42 | 43 | * **Session Five** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Esfand 25 44 | - End to End Machine Learning Project of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 45 | - Machine Learning with Scikit-Learn Part 3 46 | - Notebook: [Colab](https://colab.research.google.com/drive/1gbXt3B74FiIiI4jLlqqxTPOm-elHkYY7) 47 | 48 | * **Session Six** by [Mostafa Khodayari](https://github.com/MSTF4) was on Tuesday, Farvardin 19 49 | - Classification of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 50 | - Machine Learning with Scikit-Learn Part 4 51 | - Notebook: [Colab](https://colab.research.google.com/drive/1yxOQcpiCIGSmkgB2WwLtkI0x1QEcXKEV) 52 | 53 | * **Session Seven** by [Mostafa Khodayari](https://github.com/MSTF4) was on Tuesday, Farvardin 26 54 | - Classification of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 55 | - Machine Learning with Scikit-Learn Part 5 56 | - Notebook: [Colab](https://colab.research.google.com/drive/1yxOQcpiCIGSmkgB2WwLtkI0x1QEcXKEV) 57 | 58 | * **Session Eight** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Tuesday, Ordibehesht 2 59 | - Training Models of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 60 | - Machine Learning with Scikit-Learn Part 6 61 | - Notebook: [Colab](https://colab.research.google.com/drive/1S1RguKRlxG3jE7z1lelIX0Uk3pImr05m#scrollTo=L23wN_05sKC3) 62 | 63 | * **Session Nine** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Sunday, Ordibehesht 7 64 | 65 | - Training Models of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 66 | - Machine Learning with Scikit-Learn Part 7 67 | - Notebook: [Colab](https://colab.research.google.com/drive/1a0sKKtVQhoyIi0wY8FFqi7G3Jj7pv57B#scrollTo=KQBAFnhMxi3q) 68 | 69 | * **Session Ten** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Tuesday, Ordibehesht 16 70 | 71 | - Decision Trees of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 72 | - Machine Learning with Scikit-Learn Part 8 73 | - Notebook: [Colab](https://colab.research.google.com/drive/1yXm3xz_2GvFDz1KFAOyl20RfQTMOKCfo#scrollTo=S5QOeL61WtP5) 74 | 75 | * **Session Eleven** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Ordibehesht 23 76 | 77 | - Ensemble Learning and Random Forests of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 78 | - Machine Learning with Scikit-Learn Part 9 79 | - Notebook: [Colab](https://colab.research.google.com/drive/1JBohY2HYpWzO987OB8PUo2rOChgome5S?usp=sharing) 80 | 81 | * **Session Twelve** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Tuesday, Ordibehesht 30 82 | 83 | - Dimensionality Reduction of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 84 | - Machine Learning with Scikit-Learn Part 10 85 | - Notebook: [Colab](https://colab.research.google.com/drive/1vWRGl3V06kbyzsZFsq1TKhXbLI9fzme1#scrollTo=dTORysOwzXS_) 86 | 87 | * **Session Thirteen** by [Mostafa Khodayari](https://github.com/MSTF4) was on Tuesday, Khordad 6 88 | 89 | - Unsupervised Learning Techniques of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 90 | - Machine Learning with Scikit-Learn Part 11 91 | - Notebook: [Colab](https://colab.research.google.com/drive/1yNgQVqWZs1iKWCKaNPivuUGJtGAXePuN?usp=sharing) 92 | 93 | * **Session Fourteen** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Khordad 13 94 | 95 | - Intro. to Artificial Neural Networks with Keras of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 96 | - Machine Learning with TensorFlow and Keras Part 1 97 | - Notebook: [Colab](https://colab.research.google.com/drive/1KmKLAnhT-za1BxUj9EkKZkN5UDzd95A4?usp=sharing) 98 | 99 | * **Session Fifteen** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Khordad 25 100 | 101 | - Support Vector Machines of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 102 | - Machine Learning with Scikit-Learn Part 12 103 | - Notebook: [Colab](https://colab.research.google.com/drive/1jJcdxO0wI8QG3OVwEbiRDCOeo_knS8zY?usp=sharing) 104 | 105 | * **Session Sixteen** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Khordad 25 106 | 107 | - Intro. to Artificial Neural Networks with Keras of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 108 | - Machine Learning with TensorFlow and Keras Part 2 109 | - Selected Experiments with Artificial Neural Networks 110 | - Notebook: [Colab](https://colab.research.google.com/drive/1PRvweH1v3LFL85wnlek8RnvQMU7PRHFz?usp=sharing) 111 | 112 | # Assignments 113 | 114 | ## Assignment Set 1 115 | 116 | * Install Anaconda and Create an Environment. 117 | * Install Python Packages (Pandas, Scikit-Learn, Matplotlib) and Jupyter Lab in your new Environment. 118 | * Create a GitHub account. 119 | * Create a Repository for this Course in your GitHub. 120 | * Use Markdown to Prepare a Readme file for your Repository. 121 | * Modify (at least enter your name) and Run the [Sample Notebook](https://github.com/hhaji/Applied-Machine-Learning/blob/master/Recitation-Assignments/assignments-1_sample.ipynb) in your Environment, then Upload it in your Github Repository. 122 | 123 | -> Deadline: Saturday, Bahman 26, 23:59 (Advantage ~ 5 Points) - Extended to Sunday Bahman 27, 12:59 124 | 125 | ## Assignment Set 2 126 | 127 | * Python and Scientific Libraries: 128 | 129 | - Practice Numpy in [LabEx](https://labex.io/courses/100-numpy-exercises) 130 | - Practice Pandas in [LabEx](https://labex.io/courses/100-pandas-exercises) 131 | - Practice Matplotlib in [LabEx](https://labex.io/courses/draw-2d-and-3d-graphics-by-matplotlib) 132 | - Then Push/Upload each of the three Jupyter notebooks using git in the Labex machine. (Try to be creative!) 133 | 134 | **Please Note:** In the Labex exercises, each empty cell must be different (modified) from the sample one. Therefore, the completed notebook will have at least 200 outputs. Furthermore, please review the tutorial video for submitting the assignment set 2. 135 | 136 | -> Deadline: Saturday, Esfand 10, 23:59 (Advantage ~ 5 Points) - Extended to Saturday, Esfand 17, 23:59 137 | 138 | ## Assignment Set 3 139 | 140 | * Chapter 2 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 141 | 142 | - Exercises: 2.1, 2.2, and 2.3 143 | 144 | -> Deadline: Monday, Esfand 19, 23:59 (Announced at Esfand 12) 145 | 146 | ## Assignment Set 4 147 | 148 | * Machine Learning with Scikit-Learn: Perform a basic Machine Learning Task on the [Coronavirus/Covid-19](https://github.com/CSSEGISandData/COVID-19) Data (infected and death cases in the World and Iran). 149 | 150 | - Visualize total infected and death cases in the World and Iran over time. 151 | - Perform regressions on the data based on Scikit-learn's Linear Models: Linear and Polynomial (with different degrees) as well as at least one more method. 152 | - Fit a Gaussian function (curve) to the data. 153 | - Visualize every model and predict the total infected and death cases in the future. 154 | - Split data into a train and a test set, then repeat steps 2 to 4 using train data. 155 | - Evaluate your model predictions by comparing them to the test data. 156 | - Visualize both approaches (full and train data) for every model. 157 | - Finally: Employ everything you know about machine learning to develop a model based on the data, capable of offering optimal prediction on the total infected and death cases in the World and Iran as well as visualization over time. (Try to be creative!) 158 | 159 | **Please Note:** You can only use [Daily Reports](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports) from January 22 up to March 18. This assignment is closely related to the [Kaggle's COVID19 Global/Local US-CA Forecasting Challenges](https://www.kaggle.com/c/covid19-local-us-ca-forecasting-week-1/overview), therefore, it could also be submitted for the challenge. 160 | 161 | -> Deadline: Wednesday, Esfand 28, 23:59 (Announced at Esfand 14) 162 | 163 | ## Assignment Set 5 164 | 165 | * Chapter 3 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 166 | 167 | - Exercises: 3.2, 3.3, 3.4, 3.5, 3.6, 3.7 168 | 169 | -> Deadline: Saturday, Farvardin 9, 23:59 (Announced at Esfand 20) 170 | 171 | ## Assignment Set 6 172 | 173 | * Chapter 6 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 174 | 175 | - Exercises: 6.2, 6.4, 6.6, 6.9, 6.10, and 6.11 176 | 177 | -> Deadline: Saturday, Farvardin 16, 23:59 (Announced at Farvardin 5) 178 | 179 | ## Assignment Set 7 180 | 181 | * Chapter 9 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 182 | 183 | - Exercises: 9.1, 9.3, 9.4, and 9.6 184 | 185 | -> Deadline: Saturday, Farvardin 30, 23:59 (Announced at Farvardin 24) 186 | 187 | ## Assignment Set 8 188 | 189 | * Machine Learning with Scikit-Learn: The [dataset](https://github.com/hhaji/Applied-Machine-Learning/blob/master/Recitation-Assignments/Assignments_Data/Assignment_Set_8_Data.csv) includes the parental level of education, test preparation course, math score, reading and writing score, etc., to understand their impact on overall performance in the 5 groups of students. 190 | 191 | - Classify the specified data using the binary method and then the multi-class method to achieve an accuracy of 90% or more. 192 | 193 | -> Deadline: Saturday, Ordibehesht 13, 23:59 (Announced at Ordibehesht 1) 194 | 195 | ## Assignment Set 9 196 | 197 | * Do this exercise step by step: 198 | 199 | - Generate some nonlinear data with m=300, based on a simple quadratic equation (2nd-degree) 200 | - Fit a Polynomial Regression model to the data 201 | - Apply a 200-degree polynomial model to the preceding training data, and compare(plot them) the result with a purely linear model and a quadratic model (2nd-degree polynomial) 202 | - Plot the learning curves of the model’s performance on the training set and the validation set as a function of the training set size(or the training iteration) 203 | 204 | -> Deadline: Saturday, Ordibehesht 27, 23:59 (Announced at Ordibehesht 20) 205 | 206 | ## Assignment Set 10 207 | 208 | * Chapter 18 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 209 | 210 | - Exercises: 1 and 2 211 | 212 | * Build a decision tree model to predict survival on the Titanic [dataset](https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/titanic.csv), based on certain parameters (i.e., p-class, sex, age, and fare), then calculate the score of the model. 213 | 214 | -> Deadline: Saturday, Khordad 3, 23:59 (Announced at Ordibehesht 20) 215 | 216 | ## Assignment Set 11 217 | 218 | * Perform classification on the [MNIST](http://yann.lecun.com/exdb/mnist/), [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html), and [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html) datasets, based on the following models and compare them: 219 | 220 | - Non-Ensemble Models (e.g., Decision Tree) 221 | - Ensemble Voting Classifiers (Both Soft and Hard) 222 | - Bagging Classifier, Random Forests, and Extra Trees 223 | - AdaBoost Classifier 224 | - XGBoost Classifier 225 | 226 | * Perform regression on the [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) dataset, based on the following models and compare them: 227 | 228 | - Non-Ensemble Models (e.g., Decision Tree Regressor) 229 | - Random Forest Regressor 230 | - AdaBoost Regressor 231 | - Gradient Boosting Regressor 232 | - XGBoost Regressor 233 | 234 | * Write at least a paragraph about XGBoost and its advantages. (Optional: Advantage ~ 10 points) 235 | 236 | **Please Note**: Datasets must be downloaded and injected manually (i.e., not loading them by libraries). Furthermore, small research should be done regarding the [XGBoost](https://github.com/dmlc/xgboost); For instance, take a look at the following [blog](https://towardsdatascience.com/a-beginners-guide-to-xgboost-87f5d4c30ed7) and [tutorial](https://www.kaggle.com/dansbecker/xgboost). In addition to them, try to develop very good classifiers and regressors based on each model, then compare their performance. 237 | 238 | -> Deadline: Friday, Khordad 16, 23:59 (Announced at Khordad 4) 239 | 240 | ## Assignment Set 12 241 | 242 | * Chapter 11 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 243 | 244 | - Exercises: 1 and 2 245 | 246 | -> Deadline: Friday, Khordad 23, 23:59 (Announced at Khordad 12) 247 | 248 | ## Assignment Set 13 249 | 250 | * The [car evaluation](https://archive.ics.uci.edu/ml/datasets/Car+Evaluation) [dataset](https://github.com/hhaji/Applied-Machine-Learning/blob/master/Recitation-Assignments/Assignments_Data/Assignment_Set_13_Data.csv) includes 1,728 records with six attributes (i.e., buying, maint, doors, persons, lug_boot, and safety) and four class values. 251 | 252 | - Depending on the class feature, cluster the data using the K-Means algorithm, and then test the results using the DBSCAN and Hierarchical methods. (Hint: Use the One-Hot method to digitize the required features.) 253 | 254 | **Please Note**: You can take a look at part three of the session thirteen's Notebook. 255 | 256 | -> Deadline: Friday, Khordad 30, 23:59 (Announced at Khordad 18) 257 | 258 | ## Assignment Set 14 259 | 260 | * PCA Implementation: 261 | 262 | - Use the Mushroom classification [dataset](https://www.kaggle.com/uciml/mushroom-classification). 263 | - Import all the needed modules, which include PCA, train_test_split, and labeling and scaling tools. 264 | - Encode the data with the LabelEncoder. 265 | - Use PCA to get the list of features and plot which features have the most explanatory power, or have the most variance. 266 | - Let's convert the features into the 17 top features, then plot a scatter plot of the data point classification based on them. 267 | - Do The previous step for the top 2 features and see how the classification changes. 268 | 269 | * Singular Value Decomposition: 270 | 271 | - Write a function to load in an image and turn it into a Numpy array. 272 | - Select the red, green, and blue color channels from the image. 273 | - Compress the color channels. 274 | - Call Numpy's SVD function on the color channel we want. 275 | - Create an array of zeroes that you'll fill in after the matrix multiplication is completed. 276 | - Specify the singular value limit you want to use when doing the calculations. 277 | - Use an image to test your SVD compression on. 278 | 279 | -> Deadline: Friday, Tir 6, 23:59 (Advantage ~ 5 Points) - Friday, Tir 20, 23:59 (Announced at Tir 2) 280 | 281 | ## Assignment Set 15 282 | 283 | * Perform classification on the following datasets based on support vector machine models: 284 | 285 | - [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 286 | - [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html) 287 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 288 | 289 | * Perform regression on the following datasets based on support vector machine models: 290 | 291 | - [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) 292 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 293 | 294 | **Please Note**: Datasets must be downloaded and injected manually (i.e., not loading them by libraries). Moreover, you can find many datasets, for instance, on Kaggle. Besides, Try to develop very good classifiers and regressors based on each model. Furthermore, it would be much better to train (and experiment with) at least two models for each dataset. Careless model architectures and hyperparameter selections, which result in poor performance, will not be appreciated and may be scored very low! 295 | 296 | -> Deadline: Friday, Tir 6, 23:59 (Advantage ~ 5 Points) - Friday, Tir 20, 23:59 (Announced at Khordad 31) 297 | 298 | ## Assignment Set 16 299 | 300 | * Train neural network models (at least two different networks for each dataset, i.e., no. layers, no. neurons, activation, regularization, ...) in either Tensorflow or Pytorch to perform classification on the following datasets, then compare them with models in previous assignments: 301 | 302 | - [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 303 | - [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html) 304 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 305 | 306 | * Train neural network models (at least two different networks for each dataset, i.e., no. layers, no. neurons, activations, regularization ...) in either Tensorflow or Pytorch to perform regression on the following datasets, then compare them with models in previous assignments: 307 | 308 | - [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) 309 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 310 | 311 | * Build and implement a simple neural network in either Python or C++ (i.e., not utilizing machine learning libraries). It should be capable of having several layers and neurons as well as other hyperparameters (e.g., activations, optimizer, loss function, and regularization). Object-oriented (class/objects) programming should also be employed. Then train and compare your models with the same architecture at Tensorflow (and Keras) and Pytorch for the following data: 312 | 313 | - A High-Degree Perturbed Polynomial 314 | - [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) 315 | 316 | **Please Note**: Datasets must be downloaded and injected manually (i.e., not loading them by libraries). Moreover, you can find many datasets, for instance, on Kaggle. Besides, Try to develop very good classifiers and regressors based on each model. Careless model architectures and hyperparameter selections, which result in poor performance, will not be appreciated and may be scored very low! 317 | 318 | -> Deadline: Friday, Tir 6, 23:59 (Advantage ~ 10 Points) - Friday, Tir 20, 23:59 (Announced at Khordad 31) 319 | 320 | # Final Project 321 | 322 | * The final project will be considered as the outcome of the course, which is understanding and effective implementation of machine learning to provide practical solutions to realistic tasks. Two scenarios for the project can be imagined, applications and development of machine learning. Developing algorithms and methods is a valuable target, but may be challenging. On the other side, applications are highly recommended for this project. Students should decide on a topic for the project based on their interests as well as discussion with their mentor, who is one of the teaching assistants up to their choice. Mentors could provide advice and assistance during the topic selection and main progress. The following steps are expected: 323 | 324 | - Selecting a Mentor and Deciding on a Topic 325 | - Writing a Brief Proposal (at least two paragraphs) of the Project 326 | - Proposal Approval by the Mentor 327 | - The Main Phase of the Project 328 | - Writing and Submitting the Final Report (at least three pages) as well as Codes and Data 329 | 330 | -> Deadline: Friday, Tir 13, 23:59 (Proposal) - Friday, Mordad 3, 23:59 (Final Report and Codes) (Officially Announced at Khordad 31) 331 | 332 | **Please Note**: You can find many sample projects as well as datasets on [Kaggle](https://www.kaggle.com/). Moreover, take a look at final projects at Stanford's [CS229](http://cs229.stanford.edu/projects.html). The project will have a notable share in the final score of the course. Creativity and innovations are highly appreciated and will be rewarded accordingly. Projects will be examined by the Professor and teaching assistants. In addition to them, contacts with mentors/assistants are possible through email communications. However, in some cases, skype sessions may be arranged. 333 | 334 | **Proposal/Report Format and Submission**: The writing and structure of the two documents are important and will be evaluated. They should be in the academic/publication format. Additionally, documents must be in PDF format, which is written either with LaTeX (Advantage ~ 5 Points) or MS Office, preferably in English. You can also use available templates. You should enter documents as well as codes and data into a repository named exactly "Applied_Machine_Learning_S20_Final_Project" (you can rename it once the course ended!). Please enter the title of your project as well as the link to its repository in the following link: [Registration](https://docs.google.com/document/d/1n4WDjIZMKNghwnWzMhJKfUPSfHzUeyb4eholMpyMILY/edit?usp=sharing) 335 | 336 | **Seminar Presentation**: Monday, Tir 6, 14:15 withing the [Skype Group](https://join.skype.com/kJ6WepEDrsnt) (It is recommended to prepare at least 10 slides for the presentation!) 337 | 338 | Applied Machine Learning Seminar: 339 | 340 | 1. Pegah Khazaei: Suicide Rates Overview 1985 to 2016 341 | 2. Zahra Taheri: Spread Visualization and Prediction of the Novel Coronavirus Disease COVID-19 Using Machine Learning 342 | 3. Fateme Rahimi: Recommendation Engine 343 | 4. Mohammad Azodi: Bank-Marketing 344 | 5. Amir Mehrpanah: Apply Denoising Using Autoencoders 345 | 6. Zahra Oruji: Twitter US Airline Sentiment analysis 346 | 347 | # Submission Instruction 348 | 349 | * Please register through the link: [Registration](https://docs.google.com/document/d/1n4WDjIZMKNghwnWzMhJKfUPSfHzUeyb4eholMpyMILY/edit?usp=sharing) 350 | * Create a repository inside your Github account with the exact name "Applied_Machine_Learning_S20_Assignments". 351 | * Please review the [Projects](https://github.com/hhaji/Applied-Machine-Learning/tree/master/Projects) for further instructions. 352 | * After completing all tasks of every assignment set, add related Jupyter notebooks (and/or other files) in a folder in the repository, for instance, assignments-1.ipynb inside Assingment_Set_1 folder, for the first set. 353 | * Solutions of the exercises, as well as mathematical notations, could be written in [LaTeX](https://github.com/hhaji/Applied-Machine-Learning#latex) (Advantage ~ 10 Points), either in Jupyter notebook or PDF. MS Office or any other software (Advantage ~ 5 Points) could also be used, otherwise, images of your handwriting should be included, which is not recommended! Please acknowledge which one is used. 354 | 355 | # Scores (Assignments and Projects) 356 | 357 | * Scores are announced at Mordad 6. Please visit the [Link](https://docs.google.com/spreadsheets/d/1ygd1pvTxv3YbedejVGhXTZJ349-rLdRuPKt32WkhXCY/edit?usp=sharing) for the scores. 358 | * Each assignment set is scored from 100 points as well as extra rewards. Therefore, it can be more than 100. 359 | * **Submission after the deadline will not be accepted!** Exceptionally, Assignments 1 to 6, could be submitted after their deadline up to Farvardin 26, but it is subjected to a penalty of 30 points. 360 | * Failure to comply with the [Academic Honor Code](https://github.com/hhaji/Applied-Machine-Learning#academic-honor-code) will result in the ZERO score in each set and may have additional consequences! 361 | * The score of the final project will be calculated from 100 points. It may also be more than 100 due to the added rewards! 362 | * The final score of assignments (1 to 16, excluding the final project) will be calculated based on the weighted average of them, from 100 points. Moreover, it may be more than 100 due to the added rewards! 363 | * The final score of assignments will be calculated based on the weighted average of them, from 100 points. Moreover, it may be more than 100 due to the added rewards! 364 | * Scores may be commented. Furthermore, students could also comment on their scores and request for re-evaluation in the form of email, which will be considered at the end of the course. 365 | -------------------------------------------------------------------------------- /Recitation-Assignments/Readme.md: -------------------------------------------------------------------------------- 1 | # Teaching Assistants (Fall 2021) 2 | 3 | * (Head) [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) (Email: y.taheriyeganeh@mail.sbu.ac.ir) 4 | - Office Hours: Sundays and Wednesdays, 12 am to 1 pm, Professor's Office (TBC) 5 | * [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) (Email: r.a.erfan@gmail.com) 6 | 7 | * [Mostafa Khodayari](https://github.com/MSTF4) (Email: mo.khodayari@mail.sbu.ac.ir) 8 | 9 | * [Esmail Mafakheri](https://github.com/E008001) (Email: e.mafakheri@mail.sbu.ac.ir) 10 | 11 | **Please Note**: 12 | 13 | * A carbon copy (Cc) of your email communications with TAs must be sent to the following email address (hhhaji@yahoo.com). 14 | 15 | * Response to emails may take a few days. Please be patient! 16 | 17 | # Recitation 18 | 19 | * **Video Tutorial One** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) posted on Monday, Esfand 5 20 | - Brief on Working in Cloud-based Services, such as Google Colab 21 | - Brief Introduction of GIT 22 | - Video: Posted in the Skype Group 23 | 24 | * **Session One** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Wednesday, Esfand 7 25 | - Introduction to Python 26 | 27 | * **Session Two** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Wednesday, Esfand 7 28 | - Python Libraries Tutorial 29 | - Brief Introduction to Python Classes/Objects (Added to the Notebook!) 30 | - Notebook: [Colab](https://colab.research.google.com/drive/1pIxeznCzX16uI_ONooD644G2kwTrAFUJ) 31 | 32 | * **Session Three** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Esfand 13 33 | - The Machine Learning Landscape of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 34 | - Machine Learning with Scikit-Learn Part 1 35 | - Notebook: [Colab](https://colab.research.google.com/drive/1e_5IjXWMeJ0pq2UXneKCaT6GVfczJAoN) 36 | 37 | * **Session Four** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Esfand 20 38 | - Reviewing Session Three 39 | - End to End Machine Learning Project of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 40 | - Machine Learning with Scikit-Learn Part 2 41 | - Notebook: [Colab](https://colab.research.google.com/drive/15pmReFGAfmULTQb6RyZur9n0NCX6n8Tl) 42 | 43 | * **Session Five** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Esfand 25 44 | - End to End Machine Learning Project of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 45 | - Machine Learning with Scikit-Learn Part 3 46 | - Notebook: [Colab](https://colab.research.google.com/drive/1gbXt3B74FiIiI4jLlqqxTPOm-elHkYY7) 47 | 48 | * **Session Six** by [Mostafa Khodayari](https://github.com/MSTF4) was on Tuesday, Farvardin 19 49 | - Classification of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 50 | - Machine Learning with Scikit-Learn Part 4 51 | - Notebook: [Colab](https://colab.research.google.com/drive/1yxOQcpiCIGSmkgB2WwLtkI0x1QEcXKEV) 52 | 53 | * **Session Seven** by [Mostafa Khodayari](https://github.com/MSTF4) was on Tuesday, Farvardin 26 54 | - Classification of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 55 | - Machine Learning with Scikit-Learn Part 5 56 | - Notebook: [Colab](https://colab.research.google.com/drive/1yxOQcpiCIGSmkgB2WwLtkI0x1QEcXKEV) 57 | 58 | * **Session Eight** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Tuesday, Ordibehesht 2 59 | - Training Models of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 60 | - Machine Learning with Scikit-Learn Part 6 61 | - Notebook: [Colab](https://colab.research.google.com/drive/1S1RguKRlxG3jE7z1lelIX0Uk3pImr05m#scrollTo=L23wN_05sKC3) 62 | 63 | * **Session Nine** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Sunday, Ordibehesht 7 64 | 65 | - Training Models of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 66 | - Machine Learning with Scikit-Learn Part 7 67 | - Notebook: [Colab](https://colab.research.google.com/drive/1a0sKKtVQhoyIi0wY8FFqi7G3Jj7pv57B#scrollTo=KQBAFnhMxi3q) 68 | 69 | * **Session Ten** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Tuesday, Ordibehesht 16 70 | 71 | - Decision Trees of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 72 | - Machine Learning with Scikit-Learn Part 8 73 | - Notebook: [Colab](https://colab.research.google.com/drive/1yXm3xz_2GvFDz1KFAOyl20RfQTMOKCfo#scrollTo=S5QOeL61WtP5) 74 | 75 | * **Session Eleven** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Ordibehesht 23 76 | 77 | - Ensemble Learning and Random Forests of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 78 | - Machine Learning with Scikit-Learn Part 9 79 | - Notebook: [Colab](https://colab.research.google.com/drive/1JBohY2HYpWzO987OB8PUo2rOChgome5S?usp=sharing) 80 | 81 | * **Session Twelve** by [Erfaan Rostami Amraei](https://github.com/Erfaan-Rostami) was on Tuesday, Ordibehesht 30 82 | 83 | - Dimensionality Reduction of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 84 | - Machine Learning with Scikit-Learn Part 10 85 | - Notebook: [Colab](https://colab.research.google.com/drive/1vWRGl3V06kbyzsZFsq1TKhXbLI9fzme1#scrollTo=dTORysOwzXS_) 86 | 87 | * **Session Thirteen** by [Mostafa Khodayari](https://github.com/MSTF4) was on Tuesday, Khordad 6 88 | 89 | - Unsupervised Learning Techniques of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 90 | - Machine Learning with Scikit-Learn Part 11 91 | - Notebook: [Colab](https://colab.research.google.com/drive/1yNgQVqWZs1iKWCKaNPivuUGJtGAXePuN?usp=sharing) 92 | 93 | * **Session Fourteen** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Khordad 13 94 | 95 | - Intro. to Artificial Neural Networks with Keras of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 96 | - Machine Learning with TensorFlow and Keras Part 1 97 | - Notebook: [Colab](https://colab.research.google.com/drive/1jJcdxO0wI8QG3OVwEbiRDCOeo_knS8zY?usp=sharing) 98 | 99 | * **Session Fifteen** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Khordad 25 100 | 101 | - Support Vector Machines of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 102 | - Machine Learning with Scikit-Learn Part 12 103 | - Notebook: [Colab](https://colab.research.google.com/drive/1KmKLAnhT-za1BxUj9EkKZkN5UDzd95A4?usp=sharing) 104 | 105 | 106 | * **Session Sixteen** by [Yavar Taheri Yeganeh](https://github.com/YavarYeganeh) was on Tuesday, Khordad 25 107 | 108 | - Intro. to Artificial Neural Networks with Keras of [Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow ](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) 109 | - Machine Learning with TensorFlow and Keras Part 2 110 | - Selected Experiments with Artificial Neural Networks 111 | - Notebook: [Colab](https://colab.research.google.com/drive/1PRvweH1v3LFL85wnlek8RnvQMU7PRHFz?usp=sharing) 112 | 113 | # Assignments 114 | 115 | ## Assignment Set 1 116 | 117 | * Install Anaconda and Create an Environment. 118 | * Install Python Packages (Pandas, Scikit-Learn, Matplotlib) and Jupyter Lab in your new Environment. 119 | * Create a GitHub account. 120 | * Create a Repository for this Course in your GitHub. 121 | * Use Markdown to Prepare a Readme file for your Repository. 122 | * Modify (at least enter your name) and Run the [Sample Notebook](https://github.com/hhaji/Applied-Machine-Learning/blob/master/Recitation-Assignments/assignments-1_sample.ipynb) in your Environment, then Upload it in your Github Repository. 123 | 124 | -> Deadline: Saturday, Bahman 26, 23:59 (Advantage ~ 5 Points) - Extended to Sunday Bahman 27, 12:59 125 | 126 | ## Assignment Set 2 127 | 128 | * Python and Scientific Libraries: 129 | 130 | - Practice Numpy in [LabEx](https://labex.io/courses/100-numpy-exercises) 131 | - Practice Pandas in [LabEx](https://labex.io/courses/100-pandas-exercises) 132 | - Practice Matplotlib in [LabEx](https://labex.io/courses/draw-2d-and-3d-graphics-by-matplotlib) 133 | - Then Push/Upload each of the three Jupyter notebooks using git in the Labex machine. (Try to be creative!) 134 | 135 | **Please Note:** In the Labex exercises, each empty cell must be different (modified) from the sample one. Therefore, the completed notebook will have at least 200 outputs. Furthermore, please review the tutorial video for submitting the assignment set 2. 136 | 137 | -> Deadline: Saturday, Esfand 10, 23:59 (Advantage ~ 5 Points) - Extended to Saturday, Esfand 17, 23:59 138 | 139 | ## Assignment Set 3 140 | 141 | * Chapter 2 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 142 | 143 | - Exercises: 2.1, 2.2, and 2.3 144 | 145 | -> Deadline: Monday, Esfand 19, 23:59 (Announced at Esfand 12) 146 | 147 | ## Assignment Set 4 148 | 149 | * Machine Learning with Scikit-Learn: Perform a basic Machine Learning Task on the [Coronavirus/Covid-19](https://github.com/CSSEGISandData/COVID-19) Data (infected and death cases in the World and Iran). 150 | 151 | - Visualize total infected and death cases in the World and Iran over time. 152 | - Perform regressions on the data based on Scikit-learn's Linear Models: Linear and Polynomial (with different degrees) as well as at least one more method. 153 | - Fit a Gaussian function (curve) to the data. 154 | - Visualize every model and predict the total infected and death cases in the future. 155 | - Split data into a train and a test set, then repeat steps 2 to 4 using train data. 156 | - Evaluate your model predictions by comparing them to the test data. 157 | - Visualize both approaches (full and train data) for every model. 158 | - Finally: Employ everything you know about machine learning to develop a model based on the data, capable of offering optimal prediction on the total infected and death cases in the World and Iran as well as visualization over time. (Try to be creative!) 159 | 160 | **Please Note:** You can only use [Daily Reports](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports) from January 22 up to March 18. This assignment is closely related to the [Kaggle's COVID19 Global/Local US-CA Forecasting Challenges](https://www.kaggle.com/c/covid19-local-us-ca-forecasting-week-1/overview), therefore, it could also be submitted for the challenge. 161 | 162 | -> Deadline: Wednesday, Esfand 28, 23:59 (Announced at Esfand 14) 163 | 164 | ## Assignment Set 5 165 | 166 | * Chapter 3 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 167 | 168 | - Exercises: 3.2, 3.3, 3.4, 3.5, 3.6, 3.7 169 | 170 | -> Deadline: Saturday, Farvardin 9, 23:59 (Announced at Esfand 20) 171 | 172 | ## Assignment Set 6 173 | 174 | * Chapter 6 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 175 | 176 | - Exercises: 6.2, 6.4, 6.6, 6.9, 6.10, and 6.11 177 | 178 | -> Deadline: Saturday, Farvardin 16, 23:59 (Announced at Farvardin 5) 179 | 180 | ## Assignment Set 7 181 | 182 | * Chapter 9 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 183 | 184 | - Exercises: 9.1, 9.3, 9.4, and 9.6 185 | 186 | -> Deadline: Saturday, Farvardin 30, 23:59 (Announced at Farvardin 24) 187 | 188 | ## Assignment Set 8 189 | 190 | * Machine Learning with Scikit-Learn: The [dataset](https://github.com/hhaji/Applied-Machine-Learning/blob/master/Recitation-Assignments/Assignments_Data/Assignment_Set_8_Data.csv) includes the parental level of education, test preparation course, math score, reading and writing score, etc., to understand their impact on overall performance in the 5 groups of students. 191 | 192 | - Classify the specified data using the binary method and then the multi-class method to achieve an accuracy of 90% or more. 193 | 194 | -> Deadline: Saturday, Ordibehesht 13, 23:59 (Announced at Ordibehesht 1) 195 | 196 | ## Assignment Set 9 197 | 198 | * Do this exercise step by step: 199 | 200 | - Generate some nonlinear data with m=300, based on a simple quadratic equation (2nd-degree) 201 | - Fit a Polynomial Regression model to the data 202 | - Apply a 200-degree polynomial model to the preceding training data, and compare(plot them) the result with a purely linear model and a quadratic model (2nd-degree polynomial) 203 | - Plot the learning curves of the model’s performance on the training set and the validation set as a function of the training set size(or the training iteration) 204 | 205 | -> Deadline: Saturday, Ordibehesht 27, 23:59 (Announced at Ordibehesht 20) 206 | 207 | ## Assignment Set 10 208 | 209 | * Chapter 18 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 210 | 211 | - Exercises: 1 and 2 212 | 213 | * Build a decision tree model to predict survival on the Titanic [dataset](https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/titanic.csv), based on certain parameters (i.e., p-class, sex, age, and fare), then calculate the score of the model. 214 | 215 | -> Deadline: Saturday, Khordad 3, 23:59 (Announced at Ordibehesht 20) 216 | 217 | ## Assignment Set 11 218 | 219 | * Perform classification on the [MNIST](http://yann.lecun.com/exdb/mnist/), [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html), and [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html) datasets, based on the following models and compare them: 220 | 221 | - Non-Ensemble Models (e.g., Decision Tree) 222 | - Ensemble Voting Classifiers (Both Soft and Hard) 223 | - Bagging Classifier, Random Forests, and Extra Trees 224 | - AdaBoost Classifier 225 | - XGBoost Classifier 226 | 227 | * Perform regression on the [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) dataset, based on the following models and compare them: 228 | 229 | - Non-Ensemble Models (e.g., Decision Tree Regressor) 230 | - Random Forest Regressor 231 | - AdaBoost Regressor 232 | - Gradient Boosting Regressor 233 | - XGBoost Regressor 234 | 235 | * Write at least a paragraph about XGBoost and its advantages. (Optional: Advantage ~ 10 points) 236 | 237 | **Please Note**: Datasets must be downloaded and injected manually (i.e., not loading them by libraries). Furthermore, small research should be done regarding the [XGBoost](https://github.com/dmlc/xgboost); For instance, take a look at the following [blog](https://towardsdatascience.com/a-beginners-guide-to-xgboost-87f5d4c30ed7) and [tutorial](https://www.kaggle.com/dansbecker/xgboost). In addition to them, try to develop very good classifiers and regressors based on each model, then compare their performance. 238 | 239 | -> Deadline: Friday, Khordad 16, 23:59 (Announced at Khordad 4) 240 | 241 | ## Assignment Set 12 242 | 243 | * Chapter 11 of [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) 244 | 245 | - Exercises: 1 and 2 246 | 247 | -> Deadline: Friday, Khordad 23, 23:59 (Announced at Khordad 12) 248 | 249 | ## Assignment Set 13 250 | 251 | * The [car evaluation](https://archive.ics.uci.edu/ml/datasets/Car+Evaluation) [dataset](https://github.com/hhaji/Applied-Machine-Learning/blob/master/Recitation-Assignments/Assignments_Data/Assignment_Set_13_Data.csv) includes 1,728 records with six attributes (i.e., buying, maint, doors, persons, lug_boot, and safety) and four class values. 252 | 253 | - Depending on the class feature, cluster the data using the K-Means algorithm, and then test the results using the DBSCAN and Hierarchical methods. (Hint: Use the One-Hot method to digitize the required features.) 254 | 255 | **Please Note**: You can take a look at part three of the session thirteen's Notebook. 256 | 257 | -> Deadline: Friday, Khordad 30, 23:59 (Announced at Khordad 18) 258 | 259 | ## Assignment Set 14 260 | 261 | * PCA Implementation: 262 | 263 | - Use the Mushroom classification [dataset](https://www.kaggle.com/uciml/mushroom-classification). 264 | - Import all the needed modules, which include PCA, train_test_split, and labeling and scaling tools. 265 | - Encode the data with the LabelEncoder. 266 | - Use PCA to get the list of features and plot which features have the most explanatory power, or have the most variance. 267 | - Let's convert the features into the 17 top features, then plot a scatter plot of the data point classification based on them. 268 | - Do The previous step for the top 2 features and see how the classification changes. 269 | 270 | * Singular Value Decomposition: 271 | 272 | - Write a function to load in an image and turn it into a Numpy array. 273 | - Select the red, green, and blue color channels from the image. 274 | - Compress the color channels. 275 | - Call Numpy's SVD function on the color channel we want. 276 | - Create an array of zeroes that you'll fill in after the matrix multiplication is completed. 277 | - Specify the singular value limit you want to use when doing the calculations. 278 | - Use an image to test your SVD compression on. 279 | 280 | -> Deadline: Friday, Tir 6, 23:59 (Advantage ~ 5 Points) - Friday, Tir 20, 23:59 (Announced at Tir 2) 281 | 282 | ## Assignment Set 15 283 | 284 | * Perform classification on the following datasets based on support vector machine models: 285 | 286 | - [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 287 | - [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html) 288 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 289 | 290 | * Perform regression on the following datasets based on support vector machine models: 291 | 292 | - [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) 293 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 294 | 295 | **Please Note**: Datasets must be downloaded and injected manually (i.e., not loading them by libraries). Moreover, you can find many datasets, for instance, on Kaggle. Besides, Try to develop very good classifiers and regressors based on each model. Furthermore, it would be much better to train (and experiment with) at least two models for each dataset. Careless model architectures and hyperparameter selections, which result in poor performance, will not be appreciated and may be scored very low! 296 | 297 | -> Deadline: Friday, Tir 6, 23:59 (Advantage ~ 5 Points) - Friday, Tir 20, 23:59 (Announced at Khordad 31) 298 | 299 | ## Assignment Set 16 300 | 301 | * Train neural network models (at least two different networks for each dataset, i.e., no. layers, no. neurons, activation, regularization, ...) in either Tensorflow or Pytorch to perform classification on the following datasets, then compare them with models in previous assignments: 302 | 303 | - [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 304 | - [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html) 305 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 306 | 307 | * Train neural network models (at least two different networks for each dataset, i.e., no. layers, no. neurons, activations, regularization ...) in either Tensorflow or Pytorch to perform regression on the following datasets, then compare them with models in previous assignments: 308 | 309 | - [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) 310 | - At least one other dataset (up to your choice, but should be different from those employed in previous assignments) 311 | 312 | * Build and implement a simple neural network in either Python or C++ (i.e., not utilizing machine learning libraries). It should be capable of having several layers and neurons as well as other hyperparameters (e.g., activations, optimizer, loss function, and regularization). Object-oriented (class/objects) programming should also be employed. Then train and compare your models with the same architecture at Tensorflow (and Keras) and Pytorch for the following data: 313 | 314 | - A High-Degree Perturbed Polynomial 315 | - [California Housing Values](https://github.com/ageron/handson-ml/tree/master/datasets/housing) 316 | 317 | **Please Note**: Datasets must be downloaded and injected manually (i.e., not loading them by libraries). Moreover, you can find many datasets, for instance, on Kaggle. Besides, Try to develop very good classifiers and regressors based on each model. Careless model architectures and hyperparameter selections, which result in poor performance, will not be appreciated and may be scored very low! 318 | 319 | -> Deadline: Friday, Tir 6, 23:59 (Advantage ~ 10 Points) - Friday, Tir 20, 23:59 (Announced at Khordad 31) 320 | 321 | # Final Project 322 | 323 | * The final project will be considered as the outcome of the course, which is understanding and effective implementation of machine learning to provide practical solutions to realistic tasks. Two scenarios for the project can be imagined, applications and development of machine learning. Developing algorithms and methods is a valuable target, but may be challenging. On the other side, applications are highly recommended for this project. Students should decide on a topic for the project based on their interests as well as discussion with their mentor, who is one of the teaching assistants up to their choice. Mentors could provide advice and assistance during the topic selection and main progress. The following steps are expected: 324 | 325 | - Selecting a Mentor and Deciding on a Topic 326 | - Writing a Brief Proposal (at least two paragraphs) of the Project 327 | - Proposal Approval by the Mentor 328 | - The Main Phase of the Project 329 | - Writing and Submitting the Final Report (at least three pages) as well as Codes and Data 330 | 331 | -> Deadline: Friday, Tir 13, 23:59 (Proposal) - Friday, Mordad 3, 23:59 (Final Report and Codes) (Officially Announced at Khordad 31) 332 | 333 | **Please Note**: You can find many sample projects as well as datasets on [Kaggle](https://www.kaggle.com/). Moreover, take a look at final projects at Stanford's [CS229](http://cs229.stanford.edu/projects.html). The project will have a notable share in the final score of the course. Creativity and innovations are highly appreciated and will be rewarded accordingly. Projects will be examined by the Professor and teaching assistants. In addition to them, contacts with mentors/assistants are possible through email communications. However, in some cases, skype sessions may be arranged. 334 | 335 | **Proposal/Report Format and Submission**: The writing and structure of the two documents are important and will be evaluated. They should be in the academic/publication format. Additionally, documents must be in PDF format, which is written either with LaTeX (Advantage ~ 5 Points) or MS Office, preferably in English. You can also use available templates. You should enter documents as well as codes and data into a repository named exactly "Applied_Machine_Learning_S20_Final_Project" (you can rename it once the course ended!). Please enter the title of your project as well as the link to its repository in the following link: [Registration](https://docs.google.com/document/d/1n4WDjIZMKNghwnWzMhJKfUPSfHzUeyb4eholMpyMILY/edit?usp=sharing) 336 | 337 | **Seminar Presentation**: Monday, Tir 6, 14:15 withing the [Skype Group](https://join.skype.com/kJ6WepEDrsnt) (It is recommended to prepare at least 10 slides for the presentation!) 338 | 339 | Applied Machine Learning Seminar: 340 | 341 | 1. Pegah Khazaei: Suicide Rates Overview 1985 to 2016 342 | 2. Zahra Taheri: Spread Visualization and Prediction of the Novel Coronavirus Disease COVID-19 Using Machine Learning 343 | 3. Fateme Rahimi: Recommendation Engine 344 | 4. Mohammad Azodi: Bank-Marketing 345 | 5. Amir Mehrpanah: Apply Denoising Using Autoencoders 346 | 6. Zahra Oruji: Twitter US Airline Sentiment analysis 347 | 348 | # Submission Instruction 349 | 350 | * Please register through the link: [Registration](https://docs.google.com/document/d/1n4WDjIZMKNghwnWzMhJKfUPSfHzUeyb4eholMpyMILY/edit?usp=sharing) 351 | * Create a repository inside your Github account with the exact name "Applied_Machine_Learning_S20_Assignments". 352 | * Please review the [Projects](https://github.com/hhaji/Applied-Machine-Learning/tree/master/Projects) for further instructions. 353 | * After completing all tasks of every assignment set, add related Jupyter notebooks (and/or other files) in a folder in the repository, for instance, assignments-1.ipynb inside Assingment_Set_1 folder, for the first set. 354 | * Solutions of the exercises, as well as mathematical notations, could be written in [LaTeX](https://github.com/hhaji/Applied-Machine-Learning#latex) (Advantage ~ 10 Points), either in Jupyter notebook or PDF. MS Office or any other software (Advantage ~ 5 Points) could also be used, otherwise, images of your handwriting should be included, which is not recommended! Please acknowledge which one is used. 355 | 356 | # Scores (Assignments and Projects) 357 | 358 | * Scores are announced at Mordad 6. Please visit the [Link](https://docs.google.com/spreadsheets/d/1ygd1pvTxv3YbedejVGhXTZJ349-rLdRuPKt32WkhXCY/edit?usp=sharing) for the scores. 359 | * Each assignment set is scored from 100 points as well as extra rewards. Therefore, it can be more than 100. 360 | * **Submission after the deadline will not be accepted!** Exceptionally, Assignments 1 to 6, could be submitted after their deadline up to Farvardin 26, but it is subjected to a penalty of 30 points. 361 | * Failure to comply with the [Academic Honor Code](https://github.com/hhaji/Applied-Machine-Learning#academic-honor-code) will result in the ZERO score in each set and may have additional consequences! 362 | * The score of the final project will be calculated from 100 points. It may also be more than 100 due to the added rewards! 363 | * The final score of assignments (1 to 16, excluding the final project) will be calculated based on the weighted average of them, from 100 points. Moreover, it may be more than 100 due to the added rewards! 364 | * The final score of assignments will be calculated based on the weighted average of them, from 100 points. Moreover, it may be more than 100 due to the added rewards! 365 | * Scores may be commented. Furthermore, students could also comment on their scores and request for re-evaluation in the form of email, which will be considered at the end of the course. 366 | -------------------------------------------------------------------------------- /Tutorials/README.md: -------------------------------------------------------------------------------- 1 | # Tutorials: 2 | 3 | --- 4 | 5 | ### **Index:** 6 | - [Fascinating Blogs](#Fascinating-Blogs) 7 | - [Interesting Papers](#Interesting-Papers) 8 | - [Bias-Variance Trade-Off](#Bias-Variance-Trade-Off) 9 | - [Feature Engineering](#Feature-Engineering) 10 | - [Metric Learning](#Metric-Learning) 11 | - [Machine Learning in R](#MLR) 12 | - [Statistical Models for Machine Learning](#Statistical-models) 13 | - [Time Series](#Time-Series) 14 | - [Interpretability](#Interpretability) 15 | - [Cheat Sheets](#Cheat-Sheets) 16 | - [Videos](#Videos) 17 | 18 | --- 19 | 20 | ## Fascinating Blogs: 21 | * [Towards Data Science:](https://towardsdatascience.com/) This is a platform for data scientists to propose up-to-date content, mainly focused on data science, machine learning, artificial intelligence, and ...
22 | * [Machine Learning Crash Course from Google:](https://developers.google.com/machine-learning/crash-course/) Google's fast-paced, practical introduction to machine learning which covers building deep neural networks with TensorFlow. 23 | * [Distill](https://distill.pub/about/) is an academic journal in machine learning and it was dedicated to clear explanations of machine learning. 24 | * [Machine Learning Plus:](https://www.machinelearningplus.com) Simple and straightforward tutorials on machine learning in 25 | R and Python. 26 | * The blog of [Dawid Kopczyk:](http://dkopczyk.quantee.co.uk/category/machine_learning/) Fascinating tutorials about machine learning 27 | * The blog of [Christopher Olah:](http://colah.github.io) Fascinating tutorials about neural networks 28 | * [Machine Learning Recipe:](https://setscholars.net/category/machine-learning-recipe/) Fascinating tutorials about machine learning 29 | * [Off the Convex Path:](http://www.offconvex.org) Understanding non- convex optimization in algorithms, machine learning and nature at large 30 | * [Data Vedas:](https://www.datavedas.com) This blog was created by Rai Kapil keeping in mind the difficulties faced by people who are new to the field of data science. 31 | * [Need Help Getting Started with Applied Machine Learning?](https://machinelearningmastery.com/start-here/) by Jason Brownlee
32 | * [Awesome Data Science:](https://github.com/bulutyazilim/awesome-datascience) An open source Data Science repository to learn and apply towards solving real world problems.
33 | * [New to Data School? Start Here!](https://www.dataschool.io/start/) by Data School 34 | * [R2D3:](http://www.r2d3.us) An experiment in expressing statistical thinking with interactive design 35 | * [Machine Learning Resources](https://www.ritchieng.com/machine-learning-resources/) by Ritchie Ng 36 | * [Top 10 Machine Learning Algorithms for Beginners](https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/) 37 | 38 | ## Interesting Papers: 39 | * [A Few Useful Things to Know about Machine Learning](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf) by Pedro Domingos
40 | * [The Unreasonable Effectiveness of Data](https://static.googleusercontent.com/media/research.google.com/en/ir/pubs/archive/35179.pdf) by Alon Halevy, Peter Norvig, and Fernando Pereira
41 | * [The End of Theory: The Data Deluge Makes The Scientific Method Obsolete](https://www.wired.com/2008/06/pb-theory/) by Chris Anderson
42 | 43 | ## Bias-Variance Trade-Off: 44 | * Paper: [The Bias-Variance Dilemma](https://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents/tutorials/bias.pdf) by Raul Rojas
45 | * Blog: [Bias-Variance Tradeoff in Machine Learning](https://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/) by Satya Mallick
46 | * Blog: [Understanding the Bias-Variance Tradeoff](https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229) 47 | * Blog: [Bias and Variance in Machine Learning](https://medium.com/datadriveninvestor/bias-and-variance-in-machine-learning-51fdd38d1f86) by Renu Khandelwal 48 | * Blog: [Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning](https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/) 49 | * Blog: [A Visual Introduction to Machine Learning: Model Tuning and the Bias-Variance Trade Off](http://www.r2d3.us/visual-intro-to-machine-learning-part-2/) by Stephanie Yee and Tony Chu 50 | * Blog: [The Bias-Variance Tradeoff in Statistical Machine Learning - The Regression Setting](https://www.quantstart.com/articles/The-Bias-Variance-Tradeoff-in-Statistical-Machine-Learning-The-Regression-Setting) 51 | * NoteBook: [Exploring the Bias-Variance Tradeoff](https://github.com/justmarkham/DAT8/blob/master/notebooks/08_bias_variance.ipynb) by Kevin Markham 52 | 53 | ## Feature Engineering: 54 | * :sparkles: Blog: [Feature Engineering](https://www.datavedas.com/feature-engineering/) 55 | * :sparkles: Blog: [Selecting Statistical-Based Features in Machine Learning Application](https://hub.packtpub.com/selecting-statistical-based-features-in-machine-learning-application/) by Pravin Dhandre (This article is an excerpt from a book Feature Engineering Made Easy co-authored by Sinan Ozdemir and Divya Susarla) 56 | * :sparkles: Blog: [Feature Selection – Ten Effective Techniques with Examples (in R)](https://www.machinelearningplus.com/machine-learning/feature-selection/) 57 | * Blog: [Fundamental Techniques of Feature Engineering for Machine Learning](https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114) by Emre Rencberoglu 58 | * Blog: [How to Create Useful Features for Machine Learning](https://www.dataschool.io/introduction-to-feature-engineering/) by Kevin Markham 59 | * Blog: [Non-Mathematical Feature Engineering Techniques for Data Science](https://codesachin.wordpress.com/2016/06/25/non-mathematical-feature-engineering-techniques-for-data-science/) by Sachin Joglekar 60 | * Blog: [Feature Selection – Part I (Univariate Selection)](https://blog.datadive.net/selecting-good-features-part-i-univariate-selection/) by Ando Saabas 61 | * Blog: [Feature Selection Using Genetic Algorithms in R](https://towardsdatascience.com/feature-selection-using-genetic-algorithms-in-r-3d9252f1aa66) by Pablo Casas 62 | * Book: [Feature Engineering for Machine Learning and Data Analytics](https://www.crcpress.com/Feature-Engineering-for-Machine-Learning-and-Data-Analytics/Dong-Liu/p/book/9781138744387) by Guozhu Dong and Huan Liu 63 | * Book: [Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists](http://shop.oreilly.com/product/0636920049081.do) by By Alice Zheng and Amanda Casari 64 | * Book: [Feature Engineering Made Easy: Identify Unique Features from Your Dataset in Order to Build Powerful Machine Learning Systems ](https://www.amazon.com/Feature-Engineering-Made-Easy-Identify/dp/1787287602/ref=as_li_ss_tl?_encoding=UTF8&qid=&sr=&linkCode=sl1&tag=dataschool-20&linkId=a8a7e27a1313cd8e4f4ee3c31da79f01&language=en_US) by by Sinan Ozdemir and Divya Susarla 65 | 66 | ## Metric Learning: 67 | * Paper: [Distance Metric Learning, with Application to Clustering with Side-Information](https://ai.stanford.edu/~ang/papers/nips02-metric.pdf) 68 | * Survey: [A Survey on Metric Learning for Feature Vectors and Structured Data](https://arxiv.org/pdf/1306.6709.pdf) by Aurelien Bellet, Amaury Habrard, and Marc Sebban 69 | * Install: [Metric-Learn](https://github.com/metric-learn/metric-learn) 70 | * Example: [Metric Learning and Plotting](https://github.com/metric-learn/metric-learn/blob/master/examples/metric_plotting.ipynb) 71 | 72 | ## Machine Learning in R: 73 | * Blog: [Caret Package](https://topepo.github.io/caret/index.html) by Max Kuhn 74 | * NoteBook: [Principles of Machine Learning R](https://github.com/MicrosoftLearning/Principles-of-Machine-Learning-R) 75 | * Blog: [Caret Package – A Practical Guide to Machine Learning in R](https://www.machinelearningplus.com/machine-learning/caret-package/) 76 | * Blog: [An Introduction to Machine Learning with R](https://lgatto.github.io/IntroMachineLearningWithR/index.html) Laurent Gatto 77 | * Blog: [Practical Machine Learning Course Notes](https://sux13.github.io/DataScienceSpCourseNotes/8_PREDMACHLEARN/Practical_Machine_Learning_Course_Notes.html) by Xing Su 78 | * Cheat Sheet: [Caret Package](https://ugoproto.github.io/ugo_r_doc/caret.pdf) by Max Kuhn 79 | 80 | 81 | ## Statistical Models for Machine Learning: 82 | * Tutorial: [Poisson Regression in R](https://www.dataquest.io/blog/tutorial-poisson-regression-in-r/) by Hafsa Jabeen 83 | * Blog: [Using Linear Regression for Predictive Modeling in R](https://www.dataquest.io/blog/statistical-learning-for-predictive-modeling-r/) by Rose Martin 84 | * Tutorial: [Understanding Regression Error Metrics in Python](https://www.dataquest.io/blog/understanding-regression-error-metrics/) by Christian Pascual 85 | * Lecture: [Poisson Models for Count Data](https://data.princeton.edu/wws509/notes/c4.pdf) by Germán Rodríguez 86 | 87 | ## Time Series: 88 | * Tutorial: [Time Series Analysis with Pandas](https://www.dataquest.io/blog/tutorial-time-series-analysis-with-pandas/) by Jennifer Walker 89 | 90 | ## Interpretability: 91 | * Book: [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) by Christoph Molnar 92 | 93 | 94 | ## Cheat Sheets: 95 | * [Cheat Sheets](https://github.com/kailashahirwar/cheatsheets-ai) by Kailash Ahirwar
96 | 97 | ## Videos: 98 | * [Machine Learning Video Library - Learning From Data](https://work.caltech.edu/library/index.html) by Yaser Abu-Mostafa 99 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman 2 | title: Applied Machine Learning 3 | -------------------------------------------------------------------------------- /_layouts/default.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | {% if site.google_analytics %} 6 | 7 | 13 | {% endif %} 14 | 15 | 16 | {% seo %} 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |

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