├── .github └── workflows │ └── pages.yml ├── .gitignore ├── 404.html ├── Gemfile ├── LICENSE ├── README.md ├── _config.yml ├── _includes ├── minutes.liquid └── youtubePlayer.html ├── _layouts ├── announcement.html ├── default.html ├── minimal.html ├── module.html ├── schedule.html └── staffer.html ├── _sass ├── buttons.scss ├── color_schemes │ └── custom.scss ├── custom │ ├── announcement.scss │ ├── card.scss │ ├── custom.scss │ ├── module.scss │ ├── schedule.scss │ └── staffer.scss └── utilities │ └── _colors.scss ├── _schedules └── weekly.md ├── _staffers ├── Abtin.md ├── Ali.md ├── Saeid.md └── drfarahani.md ├── assets └── images │ ├── Abtin.jpg │ ├── AliS.jpg │ ├── Hadi-Farahani.jpg │ ├── Saeid.JPG │ ├── blank.png │ └── site-banner.JPG ├── assignments ├── Assignment 1.pdf ├── Assignment 2.pdf ├── Assignment 3.pdf └── ML Final Project.pdf ├── favicon.ico ├── index.md ├── notebooks ├── Clustering │ ├── Clustering.ipynb │ └── mall_customers.csv ├── Decision Trees │ ├── DecisionTree.ipynb │ └── penguins.csv ├── Ensemble Learning │ ├── Ensemble_Learning.ipynb │ └── images │ │ ├── bagging.png │ │ ├── boosting.png │ │ ├── ensemble.png │ │ └── stacking.png ├── ML & Scikit-learn │ ├── ML.ipynb │ └── images │ │ ├── ML.png │ │ ├── batch-vs-online.png │ │ ├── instance-vs-model.png │ │ ├── reinforcement.png │ │ ├── semi-supervised.png │ │ ├── supervised.png │ │ ├── traditional.png │ │ └── unsupervised.png ├── PCA │ ├── PCA.ipynb │ └── PCA_images │ │ ├── fig0.PNG │ │ ├── fig2.PNG │ │ └── fig3.PNG ├── Prerequisites │ ├── Linear_algebra.ipynb │ ├── datasets │ │ ├── cars.csv │ │ ├── census.csv │ │ ├── diamonds.csv │ │ ├── iris.csv │ │ ├── iris.data │ │ ├── log.csv │ │ ├── men2004.csv │ │ ├── men2008.csv │ │ ├── olympics.csv │ │ ├── summer.csv │ │ ├── tips.csv │ │ └── titanic.csv │ ├── numpy.ipynb │ ├── pandas.ipynb │ ├── pandas_part2.ipynb │ ├── pandas_part3.ipynb │ ├── visualization.ipynb │ └── vizualization_part2.ipynb ├── Regression │ ├── Linear_Regression.ipynb │ ├── Logistic_Regression.ipynb │ ├── lr_images │ │ ├── gradient_descent.PNG │ │ └── sigmoid.PNG │ └── penguins.csv └── probability & statistics │ ├── Hypothesis_Test.ipynb │ └── probability_stats_distributions.ipynb ├── pages ├── assignments │ ├── Assignment1.md │ ├── Assignment2.md │ ├── Assignment3.md │ ├── Final Project │ │ ├── Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.001.jpeg │ │ ├── Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.002.jpeg │ │ ├── Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.003.png │ │ ├── Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.004.png │ │ └── ML Final Project.md │ └── assignments.md ├── lectures │ ├── ML & Scikit-learn │ │ ├── ML.md │ │ └── images │ │ │ ├── ML.png │ │ │ ├── batch-vs-online.png │ │ │ ├── instance-vs-model.png │ │ │ ├── reinforcement.png │ │ │ ├── semi-supervised.png │ │ │ ├── supervised.png │ │ │ ├── traditional.png │ │ │ └── unsupervised.png │ ├── Prerequisites │ │ ├── Linear Algebra │ │ │ ├── Linear_algebra.md │ │ │ └── Linear_algebra_files │ │ │ │ ├── Linear_algebra_11_0.png │ │ │ │ ├── Linear_algebra_11_0.svg │ │ │ │ ├── Linear_algebra_39_0.png │ │ │ │ ├── Linear_algebra_39_0.svg │ │ │ │ ├── Linear_algebra_41_0.png │ │ │ │ ├── Linear_algebra_41_0.svg │ │ │ │ ├── Linear_algebra_45_0.png │ │ │ │ ├── Linear_algebra_45_0.svg │ │ │ │ ├── Linear_algebra_47_0.png │ │ │ │ ├── Linear_algebra_47_0.svg │ │ │ │ ├── Linear_algebra_55_0.png │ │ │ │ ├── Linear_algebra_59_1.png │ │ │ │ ├── Linear_algebra_59_1.svg │ │ │ │ ├── Linear_algebra_61_0.png │ │ │ │ └── Linear_algebra_61_0.svg │ │ ├── data visualization │ │ │ ├── visualization.md │ │ │ ├── visualization_files │ │ │ │ ├── visualization_10_1.png │ │ │ │ ├── visualization_11_1.png │ │ │ │ ├── visualization_12_1.png │ │ │ │ ├── visualization_14_1.png │ │ │ │ ├── visualization_15_1.png │ │ │ │ ├── visualization_16_1.png │ │ │ │ ├── visualization_18_1.png │ │ │ │ ├── visualization_19_1.png │ │ │ │ ├── visualization_1_1.png │ │ │ │ ├── visualization_20_1.png │ │ │ │ ├── visualization_21_1.png │ │ │ │ ├── visualization_22_1.png │ │ │ │ ├── visualization_23_1.png │ │ │ │ ├── visualization_25_0.png │ │ │ │ ├── visualization_26_0.png │ │ │ │ ├── visualization_27_1.png │ │ │ │ ├── visualization_28_0.png │ │ │ │ ├── visualization_29_0.png │ │ │ │ ├── visualization_2_1.png │ │ │ │ ├── visualization_31_1.png │ │ │ │ ├── visualization_32_0.png │ │ │ │ ├── visualization_36_0.png │ │ │ │ ├── visualization_37_0.png │ │ │ │ ├── visualization_39_1.png │ │ │ │ ├── visualization_3_1.png │ │ │ │ ├── visualization_40_0.png │ │ │ │ ├── visualization_41_0.png │ │ │ │ ├── visualization_43_1.png │ │ │ │ ├── visualization_47_0.png │ │ │ │ ├── visualization_48_1.png │ │ │ │ ├── visualization_49_1.png │ │ │ │ ├── visualization_50_1.png │ │ │ │ ├── visualization_51_1.png │ │ │ │ ├── visualization_53_1.png │ │ │ │ ├── visualization_54_1.png │ │ │ │ ├── visualization_56_1.png │ │ │ │ ├── visualization_57_1.png │ │ │ │ ├── visualization_58_1.png │ │ │ │ ├── visualization_59_1.png │ │ │ │ ├── visualization_5_1.png │ │ │ │ ├── visualization_60_1.png │ │ │ │ ├── visualization_61_1.png │ │ │ │ ├── visualization_62_1.png │ │ │ │ ├── visualization_63_1.png │ │ │ │ ├── visualization_66_1.png │ │ │ │ ├── visualization_68_1.png │ │ │ │ ├── visualization_6_1.png │ │ │ │ ├── visualization_71_0.png │ │ │ │ ├── visualization_75_1.png │ │ │ │ ├── visualization_7_2.png │ │ │ │ └── visualization_9_1.png │ │ │ ├── vizualization_part2.md │ │ │ └── vizualization_part2_files │ │ │ │ ├── vizualization_part2_11_1.png │ │ │ │ ├── vizualization_part2_12_1.png │ │ │ │ ├── vizualization_part2_13_1.png │ │ │ │ ├── vizualization_part2_15_0.png │ │ │ │ ├── vizualization_part2_16_0.png │ │ │ │ ├── vizualization_part2_18_1.png │ │ │ │ ├── vizualization_part2_19_1.png │ │ │ │ ├── vizualization_part2_21_0.png │ │ │ │ ├── vizualization_part2_23_2.png │ │ │ │ ├── vizualization_part2_24_0.png │ │ │ │ ├── vizualization_part2_25_0.png │ │ │ │ ├── vizualization_part2_26_1.png │ │ │ │ ├── vizualization_part2_28_1.png │ │ │ │ ├── vizualization_part2_30_0.png │ │ │ │ ├── vizualization_part2_31_0.png │ │ │ │ ├── vizualization_part2_33_1.png │ │ │ │ ├── vizualization_part2_34_0.png │ │ │ │ ├── vizualization_part2_36_0.png │ │ │ │ ├── vizualization_part2_37_1.png │ │ │ │ ├── vizualization_part2_38_1.png │ │ │ │ ├── vizualization_part2_39_0.png │ │ │ │ ├── vizualization_part2_3_1.png │ │ │ │ ├── vizualization_part2_6_1.png │ │ │ │ ├── vizualization_part2_8_1.png │ │ │ │ └── vizualization_part2_9_1.png │ │ ├── numpy │ │ │ └── numpy.md │ │ ├── pandas │ │ │ ├── pandas.md │ │ │ ├── pandas_files │ │ │ │ └── pandas_131_1.png │ │ │ ├── pandas_part2.md │ │ │ ├── pandas_part2_files │ │ │ │ ├── pandas_part2_172_0.png │ │ │ │ ├── pandas_part2_58_0.png │ │ │ │ ├── pandas_part2_67_0.png │ │ │ │ └── pandas_part2_86_0.png │ │ │ ├── pandas_part3.md │ │ │ └── pandas_part3_files │ │ │ │ ├── 1.png │ │ │ │ ├── 2.png │ │ │ │ ├── 3.png │ │ │ │ ├── 4.png │ │ │ │ ├── 5.png │ │ │ │ ├── 6.png │ │ │ │ ├── 7.png │ │ │ │ ├── 8.png │ │ │ │ └── 9.png │ │ └── prerequisites.md │ ├── Regression │ │ ├── Linear_Regression.md │ │ ├── Linear_Regression_files │ │ │ ├── Linear_Regression_18_0.png │ │ │ ├── Linear_Regression_21_0.png │ │ │ ├── Linear_Regression_24_0.png │ │ │ ├── Linear_Regression_33_0.png │ │ │ ├── Linear_Regression_37_0.png │ │ │ ├── Linear_Regression_41_0.png │ │ │ └── Linear_Regression_46_0.png │ │ ├── Logistic_Regression.md │ │ ├── Logistic_Regression_files │ │ │ └── Logistic_Regression_18_0.png │ │ ├── lr_images │ │ │ ├── gradient_descent.PNG │ │ │ └── sigmoid.PNG │ │ └── regression.md │ ├── Tree-Based Models │ │ ├── DecisionTree.md │ │ ├── DecisionTree_files │ │ │ ├── DecisionTree_15_0.png │ │ │ ├── DecisionTree_17_0.png │ │ │ ├── DecisionTree_21_0.png │ │ │ ├── DecisionTree_27_0.png │ │ │ ├── DecisionTree_32_0.png │ │ │ ├── DecisionTree_34_0.png │ │ │ ├── DecisionTree_37_0.png │ │ │ ├── DecisionTree_45_0.png │ │ │ ├── DecisionTree_46_0.png │ │ │ └── DecisionTree_48_0.png │ │ ├── Ensemble Learning │ │ │ ├── Ensemble_Learning.md │ │ │ ├── Ensemble_Learning_files │ │ │ │ └── Ensemble_Learning_4_1.png │ │ │ └── images │ │ │ │ ├── bagging.png │ │ │ │ ├── boosting.png │ │ │ │ ├── ensemble.png │ │ │ │ └── stacking.png │ │ └── treebasedmodels.md │ ├── Unsupervised │ │ ├── Clustering.md │ │ ├── Clustering_files │ │ │ ├── Clustering_11_1.png │ │ │ ├── Clustering_12_0.png │ │ │ ├── Clustering_15_0.png │ │ │ ├── Clustering_17_0.png │ │ │ ├── Clustering_6_0.png │ │ │ └── Clustering_9_0.png │ │ ├── PCA.md │ │ ├── PCA_images │ │ │ ├── fig0.PNG │ │ │ ├── fig2.PNG │ │ │ └── fig3.PNG │ │ └── unsupervised.md │ ├── lectures.md │ └── prop & stats │ │ ├── Hypothesis_Test.md │ │ ├── hypothesis_test_images │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 51.png │ │ ├── 6.png │ │ ├── 61.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ │ ├── prob_stats.md │ │ ├── probability_stats_distributions.md │ │ └── probability_stats_distributions_files │ │ ├── probability_stats_distributions_11_0.png │ │ ├── probability_stats_distributions_13_0.png │ │ ├── probability_stats_distributions_15_0.png │ │ ├── probability_stats_distributions_17_0.png │ │ ├── probability_stats_distributions_19_0.png │ │ ├── probability_stats_distributions_21_0.png │ │ ├── probability_stats_distributions_23_0.png │ │ ├── probability_stats_distributions_25_0.png │ │ ├── probability_stats_distributions_27_0.png │ │ ├── probability_stats_distributions_29_0.png │ │ ├── probability_stats_distributions_31_0.png │ │ ├── probability_stats_distributions_7_0.png │ │ ├── probability_stats_distributions_8_0.png │ │ └── probability_stats_distributions_9_0.png ├── resources │ ├── res.md │ ├── resources.md │ └── slides.md ├── schedule.md └── staff.md └── slides ├── 1-introduction-handout.pdf ├── 2-linear_regression-handout.pdf ├── 3-classification-handout.pdf ├── 4-cv_boot-handout.pdf ├── 5- model_selection-handout.pdf ├── Persian-handouts.rar ├── nonlinear-handout.pdf ├── statistical_learning-handout.pdf ├── svm-handout.pdf ├── trees-handout.pdf └── unsupervised-handout.pdf /.github/workflows/pages.yml: -------------------------------------------------------------------------------- 1 | # This workflow uses actions that are not certified by GitHub. 2 | # They are provided by a third-party and are governed by 3 | # separate terms of service, privacy policy, and support 4 | # documentation. 5 | 6 | # Sample workflow for building and deploying a Jekyll site to GitHub Pages 7 | name: Deploy Jekyll site to Pages 8 | 9 | on: 10 | push: 11 | branches: ["main"] 12 | 13 | # Allows you to run this workflow manually from the Actions tab 14 | workflow_dispatch: 15 | 16 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages 17 | permissions: 18 | contents: read 19 | pages: write 20 | id-token: write 21 | 22 | # Allow one concurrent deployment 23 | concurrency: 24 | group: "pages" 25 | cancel-in-progress: true 26 | 27 | jobs: 28 | # Build job 29 | build: 30 | runs-on: ubuntu-latest 31 | steps: 32 | - name: Checkout 33 | uses: actions/checkout@v3 34 | - name: Setup Ruby 35 | uses: ruby/setup-ruby@v1 36 | with: 37 | ruby-version: '3.1' # Not needed with a .ruby-version file 38 | bundler-cache: true # runs 'bundle install' and caches installed gems automatically 39 | cache-version: 0 # Increment this number if you need to re-download cached gems 40 | - name: Setup Pages 41 | id: pages 42 | uses: actions/configure-pages@v2 43 | - name: Build with Jekyll 44 | # Outputs to the './_site' directory by default 45 | run: bundle exec jekyll build --baseurl "${{ steps.pages.outputs.base_path }}" 46 | env: 47 | JEKYLL_ENV: production 48 | - name: Upload artifact 49 | # Automatically uploads an artifact from the './_site' directory by default 50 | uses: actions/upload-pages-artifact@v1 51 | 52 | # Deployment job 53 | deploy: 54 | environment: 55 | name: github-pages 56 | url: ${{ steps.deployment.outputs.page_url }} 57 | runs-on: ubuntu-latest 58 | needs: build 59 | steps: 60 | - name: Deploy to GitHub Pages 61 | id: deployment 62 | uses: actions/deploy-pages@v1 63 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.gem 2 | .bundle/ 3 | .jekyll-cache/ 4 | .jekyll-metadata 5 | .sass-cache/ 6 | Gemfile.lock 7 | _site/ 8 | node_modules/ 9 | vendor/ 10 | -------------------------------------------------------------------------------- /404.html: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: 404 4 | permalink: /404 5 | nav_exclude: true 6 | search_exclude: true 7 | --- 8 | 9 |

Page not found

10 | 11 |

12 | The page you requested could not be found. Try using the navigation {% if site.search_enabled != false %}or search {% endif %}to find what you're looking for or go back to home. 13 |

14 | -------------------------------------------------------------------------------- /Gemfile: -------------------------------------------------------------------------------- 1 | source 'https://rubygems.org' 2 | gem 'github-pages', group: :jekyll_plugins 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Kevin Lin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Course 2 | 3 | Computer Science Faculty of Shahid Beheshti University. Winter 2023 4 | 5 | Machine Learning is a rapidly evolving field that is currently revolutionizing the way we interact with data. Machine learning course will start off with the basics, such as introducing the fundamentals of statistics and probability. From there, students will move on to more advanced topics like linear and logistic regression, classification, clustering, deep learning, and reinforcement learning. By the end of the course, students should have a solid understanding of the various models and techniques used in machine learning and be able to apply them to real-world problems. 6 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | # Setup 2 | title: Machine Learning 3 | tagline: CS SBU 4 | description: Machine Learning Course of Computer Science Faculty of Shahid Beheshti University. Winter 2023 5 | baseurl: '/CS-SBU-MachineLearning-2023' # the subpath of your site, e.g. /blog 6 | url: 'https://scheshmi.github.io' # the base hostname & protocol for your site, e.g. http://example.com 7 | exclude: ["Gemfile", "Gemfile.lock", "LICENSE", "README.md",vendor] 8 | favicon_ico: "favicon.ico" 9 | # Theme settings 10 | remote_theme: pmarsceill/just-the-docs@v0.3.3 11 | color_scheme: custom 12 | search_enabled: true 13 | heading_anchors: true 14 | permalink: pretty 15 | aux_links: 16 | Repo on GitHub: 17 | - 'https://github.com/scheshmi/CS-SBU-MachineLearning-2023' 18 | footer_content: Machine Learning - Computer Science Faculty of Shahid Beheshti University. Winter 2023 - Contact us at saeidcheshmi@outlook.com 19 | 20 | # Collections for website data 21 | collections: 22 | staffers: 23 | modules: 24 | schedules: 25 | announcements: 26 | # Default layouts for each collection type 27 | defaults: 28 | - scope: 29 | path: '' 30 | type: staffers 31 | values: 32 | layout: staffer 33 | subpath: '/assets/images/' 34 | - scope: 35 | path: '' 36 | type: modules 37 | values: 38 | layout: module 39 | - scope: 40 | path: '' 41 | type: schedules 42 | values: 43 | layout: schedule 44 | - scope: 45 | path: '' 46 | type: announcements 47 | values: 48 | layout: announcement 49 | 50 | compress_html: 51 | clippings: all 52 | comments: all 53 | endings: all 54 | startings: [] 55 | blanklines: false 56 | profile: false 57 | 58 | plugins: 59 | - jekyll-seo-tag 60 | -------------------------------------------------------------------------------- /_includes/minutes.liquid: -------------------------------------------------------------------------------- 1 | {% capture _minutes_workspace %} 2 | {% comment %} 3 | Return the number of minutes between midnight and the given time string (e.g. '9:30 AM'). 4 | 5 | Parameters: 6 | `time` (string): the time to convert. 7 | {% endcomment %} 8 | 9 | {% assign _time = include.time %} 10 | {% assign _hhmm = _time | split: ' ' | first | split: ':' %} 11 | {% assign _hours = _hhmm | first | to_integer %} 12 | {% assign _minutes = _hhmm | last | to_integer %} 13 | {% assign _ampm = _time | split: ' ' | last | upcase %} 14 | 15 | {% if _ampm == 'AM' and _hours == 12 %} 16 | {% assign _hours = _hours | minus: 12 %} 17 | {% elsif _ampm == 'PM' and _hours != 12 %} 18 | {% assign _hours = _hours | plus: 12 %} 19 | {% endif %} 20 | {% endcapture %}{% assign _minutes_workspace = '' %}{{ _hours | times: 60 | plus: _minutes }} 21 | -------------------------------------------------------------------------------- /_includes/youtubePlayer.html: -------------------------------------------------------------------------------- 1 | 7 | -------------------------------------------------------------------------------- /_layouts/announcement.html: -------------------------------------------------------------------------------- 1 |
2 |

{{ page.title }}

3 | 4 | {% if page.date %} 5 | {{ page.date | date: '%b %e' }} 6 | · 7 | {% endif %} 8 | {% assign minutes = content | strip_html | number_of_words | divided_by: 180.0 | round %} 9 | {{ minutes }} min read 10 | 11 |
12 | {{ content }} 13 |
14 |
15 | -------------------------------------------------------------------------------- /_layouts/minimal.html: -------------------------------------------------------------------------------- 1 | --- 2 | layout: table_wrappers 3 | --- 4 | 5 | 6 | 7 | 8 | {% include head.html %} 9 | 10 | 11 | 12 | Link 13 | 14 | 15 | 16 | 17 | 18 | 19 | {% capture nav %} 20 | {% if site.just_the_docs.collections %} 21 | {% assign collections_size = site.just_the_docs.collections | size %} 22 | {% for collection_entry in site.just_the_docs.collections %} 23 | {% assign collection_key = collection_entry[0] %} 24 | {% assign collection_value = collection_entry[1] %} 25 | {% assign collection = site[collection_key] %} 26 | {% if collection_value.nav_exclude != true %} 27 | {% include nav.html pages=collection %} 28 | {% endif %} 29 | {% endfor %} 30 | {% else %} 31 | {% include nav.html pages=site.html_pages %} 32 | {% endif %} 33 | {% endcapture %} 34 | 35 |
36 | {% unless page.url == "/" %} 37 | {% if page.parent %} 38 | {%- for node in pages_list -%} 39 | {%- if node.parent == nil -%} 40 | {%- if page.parent == node.title or page.grand_parent == node.title -%} 41 | {%- assign first_level_url = node.url | absolute_url -%} 42 | {%- endif -%} 43 | {%- if node.has_children -%} 44 | {%- assign children_list = pages_list | where: "parent", node.title -%} 45 | {%- for child in children_list -%} 46 | {%- if page.url == child.url or page.parent == child.title -%} 47 | {%- assign second_level_url = child.url | absolute_url -%} 48 | {%- endif -%} 49 | {%- endfor -%} 50 | {%- endif -%} 51 | {%- endif -%} 52 | {%- endfor -%} 53 | 64 | {% endif %} 65 | {% endunless %} 66 |
67 | {% if site.heading_anchors != false %} 68 | {% include vendor/anchor_headings.html html=content beforeHeading="true" anchorBody="" anchorClass="anchor-heading" %} 69 | {% else %} 70 | {{ content }} 71 | {% endif %} 72 | 73 | {% if page.has_children == true and page.has_toc != false %} 74 |
75 |

Table of contents

76 | 84 | {% endif %} 85 | 86 | {% capture footer_custom %} 87 | {%- include footer_custom.html -%} 88 | {% endcapture %} 89 | {% if footer_custom != "" or site.last_edit_timestamp or site.gh_edit_link %} 90 |
91 | 119 | {% endif %} 120 | 121 |
122 |
123 | 124 | 125 | -------------------------------------------------------------------------------- /_layouts/module.html: -------------------------------------------------------------------------------- 1 |

{{ page.title }}

2 |
3 | {{ content }} 4 |
5 | -------------------------------------------------------------------------------- /_layouts/schedule.html: -------------------------------------------------------------------------------- 1 | {% assign start_time = page.timeline | first %} 2 | {% capture offset %}{% include minutes.liquid time=start_time %}{% endcapture %} 3 |
4 | 9 | 34 |
35 | -------------------------------------------------------------------------------- /_layouts/staffer.html: -------------------------------------------------------------------------------- 1 |
2 | {% if page.photo %} 3 | 4 | {% endif %} 5 |
6 |

7 | {% if page.website %} 8 | {{ page.name }} 9 | {% else %} 10 | {{ page.name }} 11 | {% endif %} 12 | {% if page.pronouns %} 13 | {{ page.pronouns }} 14 | {% endif %} 15 |

16 | {% if page.email %} 17 |

{{ page.email }}

18 | {% endif %} 19 | {% if page.section %} 20 |

Quiz Section: {{ page.section | markdownify | strip_html }}

21 | {% endif %} 22 | {% if page.office-hours %} 23 |

Office Hours: {{ page.office-hours | markdownify | strip_html }}

24 | {% endif %} 25 | {{ content }} 26 |
27 |
28 | -------------------------------------------------------------------------------- /_sass/buttons.scss: -------------------------------------------------------------------------------- 1 | // 2 | // Buttons and things that look like buttons 3 | // 4 | // stylelint-disable color-named 5 | 6 | .btn { 7 | display: inline-block; 8 | box-sizing: border-box; 9 | padding-top: 0.3em; 10 | padding-right: 1em; 11 | padding-bottom: 0.3em; 12 | padding-left: 1em; 13 | margin: 0; 14 | font-family: inherit; 15 | font-size: inherit; 16 | font-weight: 500; 17 | line-height: 1.5; 18 | color: $link-color; 19 | text-decoration: none; 20 | vertical-align: baseline; 21 | cursor: pointer; 22 | background-color: $base-button-color; 23 | border-width: 0; 24 | border-radius: $border-radius; 25 | box-shadow: 0 1px 2px rgba(0, 0, 0, 0.12), 0 3px 10px rgba(0, 0, 0, 0.08); 26 | appearance: none; 27 | 28 | &:focus { 29 | text-decoration: none; 30 | outline: none; 31 | box-shadow: 0 0 0 3px rgba(blue, 0.25); 32 | } 33 | 34 | &:focus:hover, 35 | &.selected:focus { 36 | box-shadow: 0 0 0 3px rgba(blue, 0.25); 37 | } 38 | 39 | &:hover, 40 | &.zeroclipboard-is-hover { 41 | color: darken($link-color, 2%); 42 | } 43 | 44 | &:hover, 45 | &:active, 46 | &.zeroclipboard-is-hover, 47 | &.zeroclipboard-is-active { 48 | text-decoration: none; 49 | background-color: darken($base-button-color, 1%); 50 | } 51 | 52 | &:active, 53 | &.selected, 54 | &.zeroclipboard-is-active { 55 | background-color: darken($base-button-color, 3%); 56 | background-image: none; 57 | box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15); 58 | } 59 | 60 | &.selected:hover { 61 | background-color: darken(#dcdcdc, 5%); 62 | } 63 | 64 | &:disabled, 65 | &.disabled { 66 | &, 67 | &:hover { 68 | color: rgba(102, 102, 102, 0.5); 69 | cursor: default; 70 | background-color: rgba(229, 229, 229, 0.5); 71 | background-image: none; 72 | box-shadow: none; 73 | } 74 | } 75 | } 76 | 77 | .btn-outline { 78 | color: $link-color; 79 | background: transparent; 80 | box-shadow: inset 0 0 0 2px $grey-lt-300; 81 | 82 | &:hover, 83 | &:active, 84 | &.zeroclipboard-is-hover, 85 | &.zeroclipboard-is-active { 86 | color: darken($link-color, 4%); 87 | text-decoration: none; 88 | background-color: transparent; 89 | box-shadow: inset 0 0 0 3px $grey-lt-300; 90 | } 91 | 92 | &:focus { 93 | text-decoration: none; 94 | outline: none; 95 | box-shadow: inset 0 0 0 2px $grey-dk-100, 0 0 0 3px rgba(blue, 0.25); 96 | } 97 | 98 | &:focus:hover, 99 | &.selected:focus { 100 | box-shadow: inset 0 0 0 2px $grey-dk-100; 101 | } 102 | } 103 | 104 | .btn-primary { 105 | @include btn-color($white, $btn-primary-color); 106 | } 107 | 108 | .btn-purple { 109 | @include btn-color($white, $purple-100); 110 | } 111 | 112 | .btn-blue { 113 | @include btn-color($white, $blue-000); 114 | } 115 | 116 | .btn-green { 117 | @include btn-color($white, $green-100); 118 | } 119 | 120 | .btn-i2purple { 121 | @include btn-color($white, $i2-purple); 122 | } 123 | 124 | .btn-i2blue { 125 | @include btn-color($white, $i2-blue); 126 | } 127 | 128 | .btn-i2gold { 129 | @include btn-color($white, $i2-gold); 130 | } 131 | -------------------------------------------------------------------------------- /_sass/color_schemes/custom.scss: -------------------------------------------------------------------------------- 1 | // custom hacky-ish code 2 | // .text-green-000 3 | $green-000: #7a7fe5; 4 | 5 | // custom colors 6 | $i2-purple: #6858ee; 7 | $i2-blue: #6f93cd; 8 | $i2-gold: #d6bfbe; 9 | 10 | // $body-background-color: $grey-dk-300; 11 | $sidebar-color: #f5f5ff; //$grey-dk-300; 12 | $border-color: #756bf9; 13 | 14 | // $body-text-color: $grey-lt-300; 15 | // $body-heading-color: $grey-lt-000; 16 | // $nav-child-link-color: $grey-dk-000; 17 | // $search-result-preview-color: $grey-dk-000; 18 | 19 | 20 | $link-color: #838be0; 21 | $btn-primary-color: #838be0; 22 | // $base-button-color: $grey-dk-250; 23 | 24 | // $code-background-color: $grey-dk-250; 25 | // $search-background-color: $grey-dk-250; 26 | // $table-background-color: $grey-dk-250; 27 | $feedback-color: darken($sidebar-color, 2%); 28 | -------------------------------------------------------------------------------- /_sass/custom/announcement.scss: -------------------------------------------------------------------------------- 1 | .announcement { 2 | @extend %card; 3 | 4 | h1, h2 { 5 | @extend .text-gamma; 6 | } 7 | 8 | .announcement-meta { 9 | @extend .text-epsilon; 10 | } 11 | } 12 | -------------------------------------------------------------------------------- /_sass/custom/card.scss: -------------------------------------------------------------------------------- 1 | @mixin abstract-card() { 2 | box-shadow: 0 1px 3px rgba(0, 0, 0, 0.07), 0 4px 14px rgba(0, 0, 0, 0.05); 3 | margin: $sp-4 (-$gutter-spacing-sm); 4 | 5 | @include mq(md) { 6 | border-radius: $border-radius; 7 | margin: $sp-4 0; 8 | } 9 | } 10 | 11 | %card { 12 | @include abstract-card(); 13 | display: flex; 14 | flex-direction: column; 15 | min-width: 0; 16 | padding: 0 $sp-4; 17 | position: relative; 18 | word-wrap: break-word; 19 | 20 | >:first-child { 21 | border-top: none !important; 22 | } 23 | 24 | >:last-child { 25 | border-bottom: none !important; 26 | } 27 | 28 | .label { 29 | border-radius: $border-radius; 30 | margin-left: 0; 31 | user-select: none; 32 | } 33 | } 34 | -------------------------------------------------------------------------------- /_sass/custom/custom.scss: -------------------------------------------------------------------------------- 1 | // Just the Class dependencies 2 | @import 'card'; 3 | 4 | // Just the Class styles 5 | @import 'announcement'; 6 | @import 'module'; 7 | @import 'schedule'; 8 | @import 'staffer'; 9 | 10 | // Overrides 11 | a abbr[title] { 12 | border-bottom: none; 13 | } 14 | 15 | abbr[title] { 16 | text-decoration: none; 17 | } 18 | 19 | code { 20 | font-size: 14px; 21 | padding: 0.2em 0.4em; 22 | border: none; 23 | } 24 | 25 | div.highlighter-rouge[overlay] { 26 | position: relative; 27 | 28 | &::after { 29 | @extend .label, .text-grey-dk-100; 30 | 31 | background-color: $white; 32 | border-radius: $border-radius; 33 | bottom: $sp-2; 34 | content: attr(overlay); 35 | position: absolute; 36 | right: 0; 37 | user-select: none; 38 | } 39 | } 40 | 41 | details { 42 | margin: 0 40px 1em; 43 | } 44 | 45 | h1, h2, h3, h4, h5, h6 { 46 | align-items: center; 47 | display: flex; 48 | } 49 | 50 | iframe, 51 | summary { 52 | max-width: 100%; 53 | } 54 | 55 | summary { 56 | @extend .btn, .btn-outline; 57 | } 58 | 59 | .main-content-wrap { 60 | max-width: $content-width; 61 | margin: auto; 62 | } 63 | 64 | .main-content { 65 | a { 66 | overflow-wrap: anywhere; 67 | white-space: normal; 68 | } 69 | 70 | dl { 71 | display: block; 72 | grid-template-columns: none; 73 | } 74 | 75 | dt { 76 | font-weight: 700; 77 | text-align: start; 78 | 79 | &::after { 80 | content: normal; 81 | } 82 | } 83 | 84 | dd { 85 | font-weight: normal; 86 | 87 | + dt { 88 | margin-top: 1em; 89 | } 90 | } 91 | 92 | .katex { 93 | font-size: 1.1em; 94 | } 95 | } 96 | 97 | [style*="--aspect-ratio"] > :first-child { 98 | width: 100%; 99 | } 100 | 101 | [style*="--aspect-ratio"] > img { 102 | height: auto; 103 | } 104 | 105 | @supports (--custom:property) { 106 | [style*="--aspect-ratio"] { 107 | position: relative; 108 | } 109 | 110 | [style*="--aspect-ratio"]::before { 111 | content: ""; 112 | display: block; 113 | padding-bottom: calc(100% / (var(--aspect-ratio))); 114 | } 115 | 116 | [style*="--add-height"]::before { 117 | padding-bottom: calc(100% / (var(--aspect-ratio)) + (var(--add-height))); 118 | } 119 | 120 | [style*="--aspect-ratio"] > :first-child { 121 | position: absolute; 122 | top: 0; 123 | left: 0; 124 | height: 100%; 125 | } 126 | } 127 | -------------------------------------------------------------------------------- /_sass/custom/module.scss: -------------------------------------------------------------------------------- 1 | .main-content .module, 2 | .module { 3 | @extend %card; 4 | 5 | h1, 6 | h2, 7 | h3, 8 | h4, 9 | h5, 10 | h6 { 11 | &:first-child { 12 | margin-top: $sp-4; 13 | } 14 | } 15 | 16 | >dl { 17 | border-bottom: $border $border-color; 18 | border-top: $border $border-color; 19 | display: grid; 20 | grid-template-columns: max-content 1fr; 21 | margin: $sp-2 (-$sp-4); 22 | 23 | &:first-child { 24 | margin-top: 0; 25 | } 26 | 27 | &:last-child { 28 | margin-bottom: 0; 29 | } 30 | 31 | @include mq(lg) { 32 | grid-template-columns: 1fr 7fr; 33 | } 34 | 35 | %module-item { 36 | margin: 0; 37 | padding: $sp-2; 38 | 39 | @include mq(sm) { 40 | padding: $sp-2 $sp-4; 41 | } 42 | } 43 | 44 | >dt { 45 | @extend %module-item; 46 | border-top: $border $border-color; 47 | font-weight: normal; 48 | text-align: right; 49 | 50 | +dd { 51 | border-top: $border $border-color; 52 | } 53 | 54 | &:first-child { 55 | border-top: none; 56 | 57 | +dd { 58 | border-top: none; 59 | } 60 | } 61 | 62 | &::after { 63 | content: ":"; 64 | } 65 | } 66 | 67 | >dd { 68 | @extend %module-item; 69 | 70 | +dd { 71 | padding-top: 0; 72 | } 73 | 74 | ol, ul, dl { 75 | margin: 0; 76 | } 77 | 78 | dl { 79 | display: flex; 80 | flex-direction: column; 81 | 82 | @include mq(sm) { 83 | flex-direction: row; 84 | } 85 | 86 | dt { 87 | flex: 0 0 62.5%; 88 | margin: 0; 89 | } 90 | 91 | dd { 92 | margin: 0; 93 | } 94 | } 95 | } 96 | } 97 | } 98 | -------------------------------------------------------------------------------- /_sass/custom/schedule.scss: -------------------------------------------------------------------------------- 1 | .schedule { 2 | @include abstract-card(); 3 | overflow-x: scroll; 4 | position: relative; 5 | 6 | li::before { 7 | display: none; 8 | } 9 | 10 | ul.schedule-timeline, 11 | ul.schedule-group, 12 | ul.schedule-events { 13 | margin-top: 0; 14 | padding-left: 0; 15 | } 16 | 17 | ul.schedule-timeline { 18 | margin: 40px auto 0; 19 | position: absolute; 20 | width: 100%; 21 | } 22 | 23 | .schedule-time { 24 | @extend .fs-2; 25 | color: $grey-dk-000; 26 | height: 40px; 27 | margin: 0; 28 | padding: $sp-2; 29 | position: relative; 30 | 31 | &::after { 32 | background-color: $border-color; 33 | content: ''; 34 | height: 1px; 35 | left: 0; 36 | position: absolute; 37 | top: 0; 38 | width: 100%; 39 | } 40 | } 41 | 42 | .schedule-group { 43 | display: flex; 44 | margin-bottom: 0; 45 | position: relative; 46 | } 47 | 48 | .schedule-day { 49 | border-left: $border $border-color; 50 | flex: 1 0 0; 51 | margin: 0; 52 | min-width: 130px; 53 | 54 | &:first-of-type { 55 | border-left: 0; 56 | } 57 | } 58 | 59 | h2.schedule-header { 60 | align-items: center; 61 | display: flex; 62 | font-size: 18px !important; 63 | height: 40px; 64 | justify-content: center; 65 | margin: 0; 66 | } 67 | 68 | .schedule-events { 69 | display: flex; 70 | padding: 0; 71 | position: relative; 72 | } 73 | 74 | .schedule-event { 75 | background-color: #6f93cd; 76 | border-radius: $border-radius; 77 | box-shadow: 0 10px 20px rgba(0, 0, 0, .1), inset 0 -3px 0 rgba(0, 0, 0, .2); 78 | color: $white; 79 | float: left; 80 | height: 100%; 81 | margin: 0; 82 | padding: $sp-1 $sp-2; 83 | position: absolute; 84 | width: 100%; 85 | 86 | .name { 87 | @extend .fs-3, .fw-700; 88 | } 89 | 90 | .time, 91 | .location { 92 | @extend .fs-2; 93 | } 94 | 95 | &.lecture { 96 | background-color: $grey-dk-000; 97 | } 98 | 99 | &.emergent-lang-meeting { 100 | background-color: $purple-000; 101 | } 102 | 103 | &.rl-meeting { 104 | background-color: $purple-000; 105 | } 106 | 107 | &.office-hours { 108 | background-color: $blue-000; 109 | } 110 | } 111 | } 112 | -------------------------------------------------------------------------------- /_sass/custom/staffer.scss: -------------------------------------------------------------------------------- 1 | .staffer { 2 | display: flex; 3 | margin: $sp-4; 4 | 5 | .staffer-image { 6 | border-radius: 50%; 7 | height: 100px; 8 | margin-right: $sp-4; 9 | } 10 | 11 | p, 12 | .staffer-name { 13 | margin: $sp-1 !important; 14 | } 15 | 16 | .staffer-pronouns { 17 | @extend .label, .text-grey-dk-100, .bg-grey-lt-200; 18 | } 19 | 20 | .staffer-meta { 21 | @extend .text-grey-dk-000; 22 | } 23 | } 24 | -------------------------------------------------------------------------------- /_sass/utilities/_colors.scss: -------------------------------------------------------------------------------- 1 | // 2 | // Utility classes for colors 3 | // 4 | 5 | // Text colors 6 | 7 | .text-grey-dk-000 { 8 | color: $grey-dk-000 !important; 9 | } 10 | 11 | .text-grey-dk-100 { 12 | color: $grey-dk-100 !important; 13 | } 14 | 15 | .text-grey-dk-200 { 16 | color: $grey-dk-200 !important; 17 | } 18 | 19 | .text-grey-dk-250 { 20 | color: $grey-dk-250 !important; 21 | } 22 | 23 | .text-grey-dk-300 { 24 | color: $grey-dk-300 !important; 25 | } 26 | 27 | .text-grey-lt-000 { 28 | color: $grey-lt-000 !important; 29 | } 30 | 31 | .text-grey-lt-100 { 32 | color: $grey-lt-100 !important; 33 | } 34 | 35 | .text-grey-lt-200 { 36 | color: $grey-lt-200 !important; 37 | } 38 | 39 | .text-grey-lt-300 { 40 | color: $grey-lt-300 !important; 41 | } 42 | 43 | .text-blue-000 { 44 | color: $blue-000 !important; 45 | } 46 | 47 | .text-blue-100 { 48 | color: $blue-100 !important; 49 | } 50 | 51 | .text-blue-200 { 52 | color: $blue-200 !important; 53 | } 54 | 55 | .text-blue-300 { 56 | color: $blue-300 !important; 57 | } 58 | 59 | .text-green-000 { 60 | color: $green-000 !important; 61 | } 62 | 63 | .text-green-100 { 64 | color: $green-100 !important; 65 | } 66 | 67 | .text-green-200 { 68 | color: $green-200 !important; 69 | } 70 | 71 | .text-green-300 { 72 | color: $green-300 !important; 73 | } 74 | 75 | .text-purple-000 { 76 | color: $purple-000 !important; 77 | } 78 | 79 | .text-purple-100 { 80 | color: $purple-100 !important; 81 | } 82 | 83 | .text-purple-200 { 84 | color: $purple-200 !important; 85 | } 86 | 87 | .text-purple-300 { 88 | color: $purple-300 !important; 89 | } 90 | 91 | .text-yellow-000 { 92 | color: $yellow-000 !important; 93 | } 94 | 95 | .text-yellow-100 { 96 | color: $yellow-100 !important; 97 | } 98 | 99 | .text-yellow-200 { 100 | color: $yellow-200 !important; 101 | } 102 | 103 | .text-yellow-300 { 104 | color: $yellow-300 !important; 105 | } 106 | 107 | .text-red-000 { 108 | color: $red-000 !important; 109 | } 110 | 111 | .text-red-100 { 112 | color: $red-100 !important; 113 | } 114 | 115 | .text-red-200 { 116 | color: $red-200 !important; 117 | } 118 | 119 | .text-red-300 { 120 | color: $red-300 !important; 121 | } 122 | 123 | // Background colors 124 | 125 | .bg-grey-dk-000 { 126 | background-color: $grey-dk-000 !important; 127 | } 128 | 129 | .bg-grey-dk-100 { 130 | background-color: $grey-dk-100 !important; 131 | } 132 | 133 | .bg-grey-dk-200 { 134 | background-color: $grey-dk-200 !important; 135 | } 136 | 137 | .bg-grey-dk-250 { 138 | background-color: $grey-dk-250 !important; 139 | } 140 | 141 | .bg-grey-dk-300 { 142 | background-color: $grey-dk-300 !important; 143 | } 144 | 145 | .bg-grey-lt-000 { 146 | background-color: $grey-lt-000 !important; 147 | } 148 | 149 | .bg-grey-lt-100 { 150 | background-color: $grey-lt-100 !important; 151 | } 152 | 153 | .bg-grey-lt-200 { 154 | background-color: $grey-lt-200 !important; 155 | } 156 | 157 | .bg-grey-lt-300 { 158 | background-color: $grey-lt-300 !important; 159 | } 160 | 161 | .bg-blue-000 { 162 | background-color: $blue-000 !important; 163 | } 164 | 165 | .bg-blue-100 { 166 | background-color: $blue-100 !important; 167 | } 168 | 169 | .bg-blue-200 { 170 | background-color: $blue-200 !important; 171 | } 172 | 173 | .bg-blue-300 { 174 | background-color: $blue-300 !important; 175 | } 176 | 177 | .bg-green-000 { 178 | background-color: $green-000 !important; 179 | } 180 | 181 | .bg-green-100 { 182 | background-color: $green-100 !important; 183 | } 184 | 185 | .bg-green-200 { 186 | background-color: $green-200 !important; 187 | } 188 | 189 | .bg-green-300 { 190 | background-color: $green-300 !important; 191 | } 192 | 193 | .bg-purple-000 { 194 | background-color: $purple-000 !important; 195 | } 196 | 197 | .bg-purple-100 { 198 | background-color: $purple-100 !important; 199 | } 200 | 201 | .bg-purple-200 { 202 | background-color: $purple-200 !important; 203 | } 204 | 205 | .bg-purple-300 { 206 | background-color: $purple-300 !important; 207 | } 208 | 209 | .bg-yellow-000 { 210 | background-color: $yellow-000 !important; 211 | } 212 | 213 | .bg-yellow-100 { 214 | background-color: $yellow-100 !important; 215 | } 216 | 217 | .bg-yellow-200 { 218 | background-color: $yellow-200 !important; 219 | } 220 | 221 | .bg-yellow-300 { 222 | background-color: $yellow-300 !important; 223 | } 224 | 225 | .bg-red-000 { 226 | background-color: $red-000 !important; 227 | } 228 | 229 | .bg-red-100 { 230 | background-color: $red-100 !important; 231 | } 232 | 233 | .bg-red-200 { 234 | background-color: $red-200 !important; 235 | } 236 | 237 | .bg-red-300 { 238 | background-color: $red-300 !important; 239 | } 240 | -------------------------------------------------------------------------------- /_schedules/weekly.md: -------------------------------------------------------------------------------- 1 | --- 2 | timeline: 3 | - '8:00 AM' 4 | - '8:30 AM' 5 | - '9:00 AM' 6 | - '9:30 AM' 7 | - '10:00 AM' 8 | - '10:30 AM' 9 | - '11:00 AM' 10 | - '11:30 AM' 11 | - '12:00 PM' 12 | - '12:30 PM' 13 | - '1:00 PM' 14 | - '1:30 PM' 15 | - '2:00 PM' 16 | - '2:30 PM' 17 | - '3:00 PM' 18 | schedule: 19 | - name: Saturday 20 | - name: Sunday 21 | events: 22 | - name: Foundations of Machine Learning 23 | start: 9:00 AM 24 | end: 10:30 AM 25 | location: Bachelor 26 | - name: Statistical Machine Learning 27 | start: 10:30 AM 28 | end: 12:30 PM 29 | location: Master 30 | - name: Monday 31 | - name: Tuesday 32 | events: 33 | - name: Foundations of Machine Learning 34 | start: 9:00 AM 35 | end: 10:30 AM 36 | location: Bachelor 37 | - name: Statistical Machine Learning 38 | start: 10:30 AM 39 | end: 12:30 PM 40 | location: Master 41 | - name: Wednesday 42 | --- -------------------------------------------------------------------------------- /_staffers/Abtin.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Abtin Mahyar 3 | role: Officer 4 | email: abtinmahyar@gmail.com 5 | website: https://www.linkedin.com/in/abtin-mahyar-045134218/ 6 | photo: Abtin.jpg 7 | --- 8 | 9 | Assistant 10 | {: .label .label-blue } 11 | -------------------------------------------------------------------------------- /_staffers/Ali.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Ali Sharifi 3 | role: Officer 4 | email: alisharifisearch@gmail.com 5 | website: https://github.com/alisharifi2000 6 | photo: AliS.jpg 7 | --- 8 | 9 | Assistant 10 | {: .label .label-blue } 11 | -------------------------------------------------------------------------------- /_staffers/Saeid.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Saeid Cheshmi 3 | role: Officer 4 | email: saeidcheshmi@outlook.com 5 | website: https://github.com/scheshmi 6 | photo: Saeid.JPG 7 | --- 8 | 9 | Assistant 10 | {: .label .label-blue } 11 | -------------------------------------------------------------------------------- /_staffers/drfarahani.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Dr. Hadi Farahani 3 | role: President 4 | email: h_farahani@sbu.ac.ir 5 | website: https://www.linkedin.com/in/hadi-farahani-b7614611a 6 | photo: Hadi-Farahani.jpg 7 | --- 8 | 9 | Instructor 10 | {: .label .label-yellow } 11 | -------------------------------------------------------------------------------- /assets/images/Abtin.jpg: 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/assignments/Assignment 1.pdf -------------------------------------------------------------------------------- /assignments/Assignment 2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/assignments/Assignment 2.pdf -------------------------------------------------------------------------------- /assignments/Assignment 3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/assignments/Assignment 3.pdf -------------------------------------------------------------------------------- /assignments/ML Final Project.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/assignments/ML Final Project.pdf -------------------------------------------------------------------------------- /favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/favicon.ico -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: home 3 | title: Home 4 | nav_order: 1 5 | nav_exclude: false 6 | permalink: / 7 | seo: 8 | type: Course 9 | name: Home 10 | --- 11 | 12 | # Machine Learning Course 13 | {: .mb-2 .text-green-000} 14 | 15 | Computer Science Faculty of Shahid Beheshti University. Winter 2023 16 | {: .fs-6 .fw-300 } 17 | 18 | ![](https://scheshmi.github.io/CS-SBU-MachineLearning-2023/assets/images/site-banner.JPG) 19 | 20 | Machine Learning is a rapidly evolving field that is currently revolutionizing the way we interact with data. Machine learning course will start off with the basics, such as introducing the fundamentals of statistics and probability. From there, students will move on to more advanced topics like linear and logistic regression, classification, clustering, deep learning, and reinforcement learning. By the end of the course, students should have a solid understanding of the various models and techniques used in machine learning and be able to apply them to real-world problems. 21 | 22 | 23 | 24 | ## Lectures 25 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 |
WeekTopic
1-3Fundamentals of Statistics and ProbabilityBasics of Probability
Various Distributions
Statistics
Hypothesis Tests
4-5RegressionLinear Regression
Polynomial Regression
Logistic Regression
LDA
6-8Model EvaluationCross Validation
Bootstrapping
Feature Selection
Regularization
9-10Support Vector Machine
11-13Tree-Based MethodsDecision Tree
Ensemble Learning
Bagging
Boosting
14-15Unsupervised LearningPCA
Clustering
16Reinforcement Learning
117 | 118 | -------------------------------------------------------------------------------- /notebooks/Clustering/mall_customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100) 2 | 1,Male,19,15,39 3 | 2,Male,21,15,81 4 | 3,Female,20,16,6 5 | 4,Female,23,16,77 6 | 5,Female,31,17,40 7 | 6,Female,22,17,76 8 | 7,Female,35,18,6 9 | 8,Female,23,18,94 10 | 9,Male,64,19,3 11 | 10,Female,30,19,72 12 | 11,Male,67,19,14 13 | 12,Female,35,19,99 14 | 13,Female,58,20,15 15 | 14,Female,24,20,77 16 | 15,Male,37,20,13 17 | 16,Male,22,20,79 18 | 17,Female,35,21,35 19 | 18,Male,20,21,66 20 | 19,Male,52,23,29 21 | 20,Female,35,23,98 22 | 21,Male,35,24,35 23 | 22,Male,25,24,73 24 | 23,Female,46,25,5 25 | 24,Male,31,25,73 26 | 25,Female,54,28,14 27 | 26,Male,29,28,82 28 | 27,Female,45,28,32 29 | 28,Male,35,28,61 30 | 29,Female,40,29,31 31 | 30,Female,23,29,87 32 | 31,Male,60,30,4 33 | 32,Female,21,30,73 34 | 33,Male,53,33,4 35 | 34,Male,18,33,92 36 | 35,Female,49,33,14 37 | 36,Female,21,33,81 38 | 37,Female,42,34,17 39 | 38,Female,30,34,73 40 | 39,Female,36,37,26 41 | 40,Female,20,37,75 42 | 41,Female,65,38,35 43 | 42,Male,24,38,92 44 | 43,Male,48,39,36 45 | 44,Female,31,39,61 46 | 45,Female,49,39,28 47 | 46,Female,24,39,65 48 | 47,Female,50,40,55 49 | 48,Female,27,40,47 50 | 49,Female,29,40,42 51 | 50,Female,31,40,42 52 | 51,Female,49,42,52 53 | 52,Male,33,42,60 54 | 53,Female,31,43,54 55 | 54,Male,59,43,60 56 | 55,Female,50,43,45 57 | 56,Male,47,43,41 58 | 57,Female,51,44,50 59 | 58,Male,69,44,46 60 | 59,Female,27,46,51 61 | 60,Male,53,46,46 62 | 61,Male,70,46,56 63 | 62,Male,19,46,55 64 | 63,Female,67,47,52 65 | 64,Female,54,47,59 66 | 65,Male,63,48,51 67 | 66,Male,18,48,59 68 | 67,Female,43,48,50 69 | 68,Female,68,48,48 70 | 69,Male,19,48,59 71 | 70,Female,32,48,47 72 | 71,Male,70,49,55 73 | 72,Female,47,49,42 74 | 73,Female,60,50,49 75 | 74,Female,60,50,56 76 | 75,Male,59,54,47 77 | 76,Male,26,54,54 78 | 77,Female,45,54,53 79 | 78,Male,40,54,48 80 | 79,Female,23,54,52 81 | 80,Female,49,54,42 82 | 81,Male,57,54,51 83 | 82,Male,38,54,55 84 | 83,Male,67,54,41 85 | 84,Female,46,54,44 86 | 85,Female,21,54,57 87 | 86,Male,48,54,46 88 | 87,Female,55,57,58 89 | 88,Female,22,57,55 90 | 89,Female,34,58,60 91 | 90,Female,50,58,46 92 | 91,Female,68,59,55 93 | 92,Male,18,59,41 94 | 93,Male,48,60,49 95 | 94,Female,40,60,40 96 | 95,Female,32,60,42 97 | 96,Male,24,60,52 98 | 97,Female,47,60,47 99 | 98,Female,27,60,50 100 | 99,Male,48,61,42 101 | 100,Male,20,61,49 102 | 101,Female,23,62,41 103 | 102,Female,49,62,48 104 | 103,Male,67,62,59 105 | 104,Male,26,62,55 106 | 105,Male,49,62,56 107 | 106,Female,21,62,42 108 | 107,Female,66,63,50 109 | 108,Male,54,63,46 110 | 109,Male,68,63,43 111 | 110,Male,66,63,48 112 | 111,Male,65,63,52 113 | 112,Female,19,63,54 114 | 113,Female,38,64,42 115 | 114,Male,19,64,46 116 | 115,Female,18,65,48 117 | 116,Female,19,65,50 118 | 117,Female,63,65,43 119 | 118,Female,49,65,59 120 | 119,Female,51,67,43 121 | 120,Female,50,67,57 122 | 121,Male,27,67,56 123 | 122,Female,38,67,40 124 | 123,Female,40,69,58 125 | 124,Male,39,69,91 126 | 125,Female,23,70,29 127 | 126,Female,31,70,77 128 | 127,Male,43,71,35 129 | 128,Male,40,71,95 130 | 129,Male,59,71,11 131 | 130,Male,38,71,75 132 | 131,Male,47,71,9 133 | 132,Male,39,71,75 134 | 133,Female,25,72,34 135 | 134,Female,31,72,71 136 | 135,Male,20,73,5 137 | 136,Female,29,73,88 138 | 137,Female,44,73,7 139 | 138,Male,32,73,73 140 | 139,Male,19,74,10 141 | 140,Female,35,74,72 142 | 141,Female,57,75,5 143 | 142,Male,32,75,93 144 | 143,Female,28,76,40 145 | 144,Female,32,76,87 146 | 145,Male,25,77,12 147 | 146,Male,28,77,97 148 | 147,Male,48,77,36 149 | 148,Female,32,77,74 150 | 149,Female,34,78,22 151 | 150,Male,34,78,90 152 | 151,Male,43,78,17 153 | 152,Male,39,78,88 154 | 153,Female,44,78,20 155 | 154,Female,38,78,76 156 | 155,Female,47,78,16 157 | 156,Female,27,78,89 158 | 157,Male,37,78,1 159 | 158,Female,30,78,78 160 | 159,Male,34,78,1 161 | 160,Female,30,78,73 162 | 161,Female,56,79,35 163 | 162,Female,29,79,83 164 | 163,Male,19,81,5 165 | 164,Female,31,81,93 166 | 165,Male,50,85,26 167 | 166,Female,36,85,75 168 | 167,Male,42,86,20 169 | 168,Female,33,86,95 170 | 169,Female,36,87,27 171 | 170,Male,32,87,63 172 | 171,Male,40,87,13 173 | 172,Male,28,87,75 174 | 173,Male,36,87,10 175 | 174,Male,36,87,92 176 | 175,Female,52,88,13 177 | 176,Female,30,88,86 178 | 177,Male,58,88,15 179 | 178,Male,27,88,69 180 | 179,Male,59,93,14 181 | 180,Male,35,93,90 182 | 181,Female,37,97,32 183 | 182,Female,32,97,86 184 | 183,Male,46,98,15 185 | 184,Female,29,98,88 186 | 185,Female,41,99,39 187 | 186,Male,30,99,97 188 | 187,Female,54,101,24 189 | 188,Male,28,101,68 190 | 189,Female,41,103,17 191 | 190,Female,36,103,85 192 | 191,Female,34,103,23 193 | 192,Female,32,103,69 194 | 193,Male,33,113,8 195 | 194,Female,38,113,91 196 | 195,Female,47,120,16 197 | 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-------------------------------------------------------------------------------- 1 | YEAR,Make,Model,Size,(kW),Unnamed: 5,TYPE,CITY (kWh/100 km),HWY (kWh/100 km),COMB (kWh/100 km),CITY (Le/100 km),HWY (Le/100 km),COMB (Le/100 km),(g/km),RATING,(km),TIME (h) 2 | 2012,MITSUBISHI,i-MiEV,SUBCOMPACT,49,A1,B,16.9,21.4,18.7,1.9,2.4,2.1,0,n/a,100,7 3 | 2012,NISSAN,LEAF,MID-SIZE,80,A1,B,19.3,23.0,21.1,2.2,2.6,2.4,0,n/a,117,7 4 | 2013,FORD,FOCUS ELECTRIC,COMPACT,107,A1,B,19.0,21.1,20.0,2.1,2.4,2.2,0,n/a,122,4 5 | 2013,MITSUBISHI,i-MiEV,SUBCOMPACT,49,A1,B,16.9,21.4,18.7,1.9,2.4,2.1,0,n/a,100,7 6 | 2013,NISSAN,LEAF,MID-SIZE,80,A1,B,19.3,23.0,21.1,2.2,2.6,2.4,0,n/a,117,7 7 | 2013,SMART,FORTWO ELECTRIC DRIVE CABRIOLET,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,n/a,109,8 8 | 2013,SMART,FORTWO ELECTRIC DRIVE COUPE,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,n/a,109,8 9 | 2013,TESLA,MODEL S (40 kWh battery),FULL-SIZE,270,A1,B,22.4,21.9,22.2,2.5,2.5,2.5,0,n/a,224,6 10 | 2013,TESLA,MODEL S (60 kWh battery),FULL-SIZE,270,A1,B,22.2,21.7,21.9,2.5,2.4,2.5,0,n/a,335,10 11 | 2013,TESLA,MODEL S (85 kWh battery),FULL-SIZE,270,A1,B,23.8,23.2,23.6,2.7,2.6,2.6,0,n/a,426,12 12 | 2013,TESLA,MODEL S PERFORMANCE,FULL-SIZE,310,A1,B,23.9,23.2,23.6,2.7,2.6,2.6,0,n/a,426,12 13 | 2014,CHEVROLET,SPARK EV,SUBCOMPACT,104,A1,B,16.0,19.6,17.8,1.8,2.2,2.0,0,n/a,131,7 14 | 2014,FORD,FOCUS ELECTRIC,COMPACT,107,A1,B,19.0,21.1,20.0,2.1,2.4,2.2,0,n/a,122,4 15 | 2014,MITSUBISHI,i-MiEV,SUBCOMPACT,49,A1,B,16.9,21.4,18.7,1.9,2.4,2.1,0,n/a,100,7 16 | 2014,NISSAN,LEAF,MID-SIZE,80,A1,B,16.5,20.8,18.4,1.9,2.3,2.1,0,n/a,135,5 17 | 2014,SMART,FORTWO ELECTRIC DRIVE CABRIOLET,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,n/a,109,8 18 | 2014,SMART,FORTWO ELECTRIC DRIVE COUPE,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,n/a,109,8 19 | 2014,TESLA,MODEL S (60 kWh battery),FULL-SIZE,225,A1,B,22.2,21.7,21.9,2.5,2.4,2.5,0,n/a,335,10 20 | 2014,TESLA,MODEL S (85 kWh battery),FULL-SIZE,270,A1,B,23.8,23.2,23.6,2.7,2.6,2.6,0,n/a,426,12 21 | 2014,TESLA,MODEL S PERFORMANCE,FULL-SIZE,310,A1,B,23.9,23.2,23.6,2.7,2.6,2.6,0,n/a,426,12 22 | 2015,BMW,i3,SUBCOMPACT,125,A1,B,15.2,18.8,16.8,1.7,2.1,1.9,0,n/a,130,4 23 | 2015,CHEVROLET,SPARK EV,SUBCOMPACT,104,A1,B,16.0,19.6,17.8,1.8,2.2,2.0,0,n/a,131,7 24 | 2015,FORD,FOCUS ELECTRIC,COMPACT,107,A1,B,19.0,21.1,20.0,2.1,2.4,2.2,0,n/a,122,4 25 | 2015,KIA,SOUL EV,STATION WAGON - SMALL,81,A1,B,17.5,22.7,19.9,2.0,2.6,2.2,0,n/a,149,4 26 | 2015,MITSUBISHI,i-MiEV,SUBCOMPACT,49,A1,B,16.9,21.4,18.7,1.9,2.4,2.1,0,n/a,100,7 27 | 2015,NISSAN,LEAF,MID-SIZE,80,A1,B,16.5,20.8,18.4,1.9,2.3,2.1,0,n/a,135,5 28 | 2015,SMART,FORTWO ELECTRIC DRIVE CABRIOLET,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,n/a,109,8 29 | 2015,SMART,FORTWO ELECTRIC DRIVE COUPE,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,n/a,109,8 30 | 2015,TESLA,MODEL S (60 kWh battery),FULL-SIZE,283,A1,B,22.2,21.7,21.9,2.5,2.4,2.5,0,n/a,335,10 31 | 2015,TESLA,MODEL S (70 kWh battery),FULL-SIZE,283,A1,B,23.8,23.2,23.6,2.7,2.6,2.6,0,n/a,377,12 32 | 2015,TESLA,MODEL S (85/90 kWh battery),FULL-SIZE,283,A1,B,23.8,23.2,23.6,2.7,2.6,2.6,0,n/a,426,12 33 | 2015,TESLA,MODEL S 70D,FULL-SIZE,280,A1,B,20.8,20.6,20.7,2.3,2.3,2.3,0,n/a,386,12 34 | 2015,TESLA,MODEL S 85D/90D,FULL-SIZE,280,A1,B,22.0,19.8,21.0,2.5,2.2,2.4,0,n/a,435,12 35 | 2015,TESLA,MODEL S P85D/P90D,FULL-SIZE,515,A1,B,23.4,21.5,22.5,2.6,2.4,2.5,0,n/a,407,12 36 | 2016,BMW,i3,SUBCOMPACT,125,A1,B,15.2,18.8,16.8,1.7,2.1,1.9,0,10,130,4 37 | 2016,CHEVROLET,SPARK EV,SUBCOMPACT,104,A1,B,16.0,19.6,17.8,1.8,2.2,2.0,0,10,131,7 38 | 2016,FORD,FOCUS ELECTRIC,COMPACT,107,A1,B,19.0,21.1,20.0,2.1,2.4,2.2,0,10,122,4 39 | 2016,KIA,SOUL EV,STATION WAGON - SMALL,81,A1,B,17.5,22.7,19.9,2.0,2.6,2.2,0,10,149,4 40 | 2016,MITSUBISHI,i-MiEV,SUBCOMPACT,49,A1,B,16.9,21.4,18.7,1.9,2.4,2.1,0,10,100,7 41 | 2016,NISSAN,LEAF (24 kWh battery),MID-SIZE,80,A1,B,16.5,20.8,18.4,1.9,2.3,2.1,0,10,135,5 42 | 2016,NISSAN,LEAF (30 kWh battery),MID-SIZE,80,A1,B,17.0,20.7,18.6,1.9,2.3,2.1,0,10,172,6 43 | 2016,SMART,FORTWO ELECTRIC DRIVE CABRIOLET,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,10,109,8 44 | 2016,SMART,FORTWO ELECTRIC DRIVE COUPE,TWO-SEATER,35,A1,B,17.2,22.5,19.6,1.9,2.5,2.2,0,10,109,8 45 | 2016,TESLA,MODEL S (60 kWh battery),FULL-SIZE,283,A1,B,22.2,21.7,21.9,2.5,2.4,2.5,0,10,335,10 46 | 2016,TESLA,MODEL S (70 kWh battery),FULL-SIZE,283,A1,B,23.8,23.2,23.6,2.7,2.6,2.6,0,10,377,12 47 | 2016,TESLA,MODEL S (85/90 kWh battery),FULL-SIZE,283,A1,B,23.8,23.2,23.6,2.7,2.6,2.6,0,10,426,12 48 | 2016,TESLA,MODEL S 70D,FULL-SIZE,386,A1,B,20.8,20.6,20.7,2.3,2.3,2.3,0,10,386,12 49 | 2016,TESLA,MODEL S 85D/90D,FULL-SIZE,386,A1,B,22.0,19.8,21.0,2.5,2.2,2.4,0,10,435,12 50 | 2016,TESLA,MODEL S 90D (Refresh),FULL-SIZE,386,A1,B,20.8,19.7,20.3,2.3,2.2,2.3,0,10,473,12 51 | 2016,TESLA,MODEL S P85D/P90D,FULL-SIZE,568,A1,B,23.4,21.5,22.5,2.6,2.4,2.5,0,10,407,12 52 | 2016,TESLA,MODEL S P90D (Refresh),FULL-SIZE,568,A1,B,22.9,21.0,22.1,2.6,2.4,2.5,0,10,435,12 53 | 2016,TESLA,MODEL X 90D,SUV - STANDARD,386,A1,B,23.2,22.2,22.7,2.6,2.5,2.6,0,10,414,12 54 | 2016,TESLA,MODEL X P90D,SUV - STANDARD,568,A1,B,23.6,23.3,23.5,2.7,2.6,2.6,0,10,402,12 55 | -------------------------------------------------------------------------------- /notebooks/Prerequisites/datasets/iris.csv: -------------------------------------------------------------------------------- 1 | "sepal.length","sepal.width","petal.length","petal.width","variety" 2 | 5.1,3.5,1.4,.2,"Setosa" 3 | 4.9,3,1.4,.2,"Setosa" 4 | 4.7,3.2,1.3,.2,"Setosa" 5 | 4.6,3.1,1.5,.2,"Setosa" 6 | 5,3.6,1.4,.2,"Setosa" 7 | 5.4,3.9,1.7,.4,"Setosa" 8 | 4.6,3.4,1.4,.3,"Setosa" 9 | 5,3.4,1.5,.2,"Setosa" 10 | 4.4,2.9,1.4,.2,"Setosa" 11 | 4.9,3.1,1.5,.1,"Setosa" 12 | 5.4,3.7,1.5,.2,"Setosa" 13 | 4.8,3.4,1.6,.2,"Setosa" 14 | 4.8,3,1.4,.1,"Setosa" 15 | 4.3,3,1.1,.1,"Setosa" 16 | 5.8,4,1.2,.2,"Setosa" 17 | 5.7,4.4,1.5,.4,"Setosa" 18 | 5.4,3.9,1.3,.4,"Setosa" 19 | 5.1,3.5,1.4,.3,"Setosa" 20 | 5.7,3.8,1.7,.3,"Setosa" 21 | 5.1,3.8,1.5,.3,"Setosa" 22 | 5.4,3.4,1.7,.2,"Setosa" 23 | 5.1,3.7,1.5,.4,"Setosa" 24 | 4.6,3.6,1,.2,"Setosa" 25 | 5.1,3.3,1.7,.5,"Setosa" 26 | 4.8,3.4,1.9,.2,"Setosa" 27 | 5,3,1.6,.2,"Setosa" 28 | 5,3.4,1.6,.4,"Setosa" 29 | 5.2,3.5,1.5,.2,"Setosa" 30 | 5.2,3.4,1.4,.2,"Setosa" 31 | 4.7,3.2,1.6,.2,"Setosa" 32 | 4.8,3.1,1.6,.2,"Setosa" 33 | 5.4,3.4,1.5,.4,"Setosa" 34 | 5.2,4.1,1.5,.1,"Setosa" 35 | 5.5,4.2,1.4,.2,"Setosa" 36 | 4.9,3.1,1.5,.2,"Setosa" 37 | 5,3.2,1.2,.2,"Setosa" 38 | 5.5,3.5,1.3,.2,"Setosa" 39 | 4.9,3.6,1.4,.1,"Setosa" 40 | 4.4,3,1.3,.2,"Setosa" 41 | 5.1,3.4,1.5,.2,"Setosa" 42 | 5,3.5,1.3,.3,"Setosa" 43 | 4.5,2.3,1.3,.3,"Setosa" 44 | 4.4,3.2,1.3,.2,"Setosa" 45 | 5,3.5,1.6,.6,"Setosa" 46 | 5.1,3.8,1.9,.4,"Setosa" 47 | 4.8,3,1.4,.3,"Setosa" 48 | 5.1,3.8,1.6,.2,"Setosa" 49 | 4.6,3.2,1.4,.2,"Setosa" 50 | 5.3,3.7,1.5,.2,"Setosa" 51 | 5,3.3,1.4,.2,"Setosa" 52 | 7,3.2,4.7,1.4,"Versicolor" 53 | 6.4,3.2,4.5,1.5,"Versicolor" 54 | 6.9,3.1,4.9,1.5,"Versicolor" 55 | 5.5,2.3,4,1.3,"Versicolor" 56 | 6.5,2.8,4.6,1.5,"Versicolor" 57 | 5.7,2.8,4.5,1.3,"Versicolor" 58 | 6.3,3.3,4.7,1.6,"Versicolor" 59 | 4.9,2.4,3.3,1,"Versicolor" 60 | 6.6,2.9,4.6,1.3,"Versicolor" 61 | 5.2,2.7,3.9,1.4,"Versicolor" 62 | 5,2,3.5,1,"Versicolor" 63 | 5.9,3,4.2,1.5,"Versicolor" 64 | 6,2.2,4,1,"Versicolor" 65 | 6.1,2.9,4.7,1.4,"Versicolor" 66 | 5.6,2.9,3.6,1.3,"Versicolor" 67 | 6.7,3.1,4.4,1.4,"Versicolor" 68 | 5.6,3,4.5,1.5,"Versicolor" 69 | 5.8,2.7,4.1,1,"Versicolor" 70 | 6.2,2.2,4.5,1.5,"Versicolor" 71 | 5.6,2.5,3.9,1.1,"Versicolor" 72 | 5.9,3.2,4.8,1.8,"Versicolor" 73 | 6.1,2.8,4,1.3,"Versicolor" 74 | 6.3,2.5,4.9,1.5,"Versicolor" 75 | 6.1,2.8,4.7,1.2,"Versicolor" 76 | 6.4,2.9,4.3,1.3,"Versicolor" 77 | 6.6,3,4.4,1.4,"Versicolor" 78 | 6.8,2.8,4.8,1.4,"Versicolor" 79 | 6.7,3,5,1.7,"Versicolor" 80 | 6,2.9,4.5,1.5,"Versicolor" 81 | 5.7,2.6,3.5,1,"Versicolor" 82 | 5.5,2.4,3.8,1.1,"Versicolor" 83 | 5.5,2.4,3.7,1,"Versicolor" 84 | 5.8,2.7,3.9,1.2,"Versicolor" 85 | 6,2.7,5.1,1.6,"Versicolor" 86 | 5.4,3,4.5,1.5,"Versicolor" 87 | 6,3.4,4.5,1.6,"Versicolor" 88 | 6.7,3.1,4.7,1.5,"Versicolor" 89 | 6.3,2.3,4.4,1.3,"Versicolor" 90 | 5.6,3,4.1,1.3,"Versicolor" 91 | 5.5,2.5,4,1.3,"Versicolor" 92 | 5.5,2.6,4.4,1.2,"Versicolor" 93 | 6.1,3,4.6,1.4,"Versicolor" 94 | 5.8,2.6,4,1.2,"Versicolor" 95 | 5,2.3,3.3,1,"Versicolor" 96 | 5.6,2.7,4.2,1.3,"Versicolor" 97 | 5.7,3,4.2,1.2,"Versicolor" 98 | 5.7,2.9,4.2,1.3,"Versicolor" 99 | 6.2,2.9,4.3,1.3,"Versicolor" 100 | 5.1,2.5,3,1.1,"Versicolor" 101 | 5.7,2.8,4.1,1.3,"Versicolor" 102 | 6.3,3.3,6,2.5,"Virginica" 103 | 5.8,2.7,5.1,1.9,"Virginica" 104 | 7.1,3,5.9,2.1,"Virginica" 105 | 6.3,2.9,5.6,1.8,"Virginica" 106 | 6.5,3,5.8,2.2,"Virginica" 107 | 7.6,3,6.6,2.1,"Virginica" 108 | 4.9,2.5,4.5,1.7,"Virginica" 109 | 7.3,2.9,6.3,1.8,"Virginica" 110 | 6.7,2.5,5.8,1.8,"Virginica" 111 | 7.2,3.6,6.1,2.5,"Virginica" 112 | 6.5,3.2,5.1,2,"Virginica" 113 | 6.4,2.7,5.3,1.9,"Virginica" 114 | 6.8,3,5.5,2.1,"Virginica" 115 | 5.7,2.5,5,2,"Virginica" 116 | 5.8,2.8,5.1,2.4,"Virginica" 117 | 6.4,3.2,5.3,2.3,"Virginica" 118 | 6.5,3,5.5,1.8,"Virginica" 119 | 7.7,3.8,6.7,2.2,"Virginica" 120 | 7.7,2.6,6.9,2.3,"Virginica" 121 | 6,2.2,5,1.5,"Virginica" 122 | 6.9,3.2,5.7,2.3,"Virginica" 123 | 5.6,2.8,4.9,2,"Virginica" 124 | 7.7,2.8,6.7,2,"Virginica" 125 | 6.3,2.7,4.9,1.8,"Virginica" 126 | 6.7,3.3,5.7,2.1,"Virginica" 127 | 7.2,3.2,6,1.8,"Virginica" 128 | 6.2,2.8,4.8,1.8,"Virginica" 129 | 6.1,3,4.9,1.8,"Virginica" 130 | 6.4,2.8,5.6,2.1,"Virginica" 131 | 7.2,3,5.8,1.6,"Virginica" 132 | 7.4,2.8,6.1,1.9,"Virginica" 133 | 7.9,3.8,6.4,2,"Virginica" 134 | 6.4,2.8,5.6,2.2,"Virginica" 135 | 6.3,2.8,5.1,1.5,"Virginica" 136 | 6.1,2.6,5.6,1.4,"Virginica" 137 | 7.7,3,6.1,2.3,"Virginica" 138 | 6.3,3.4,5.6,2.4,"Virginica" 139 | 6.4,3.1,5.5,1.8,"Virginica" 140 | 6,3,4.8,1.8,"Virginica" 141 | 6.9,3.1,5.4,2.1,"Virginica" 142 | 6.7,3.1,5.6,2.4,"Virginica" 143 | 6.9,3.1,5.1,2.3,"Virginica" 144 | 5.8,2.7,5.1,1.9,"Virginica" 145 | 6.8,3.2,5.9,2.3,"Virginica" 146 | 6.7,3.3,5.7,2.5,"Virginica" 147 | 6.7,3,5.2,2.3,"Virginica" 148 | 6.3,2.5,5,1.9,"Virginica" 149 | 6.5,3,5.2,2,"Virginica" 150 | 6.2,3.4,5.4,2.3,"Virginica" 151 | 5.9,3,5.1,1.8,"Virginica" -------------------------------------------------------------------------------- /notebooks/Prerequisites/datasets/iris.data: -------------------------------------------------------------------------------- 1 | 5.1,3.5,1.4,0.2,Iris-setosa 2 | 4.9,3.0,1.4,0.2,Iris-setosa 3 | 4.7,3.2,1.3,0.2,Iris-setosa 4 | 4.6,3.1,1.5,0.2,Iris-setosa 5 | 5.0,3.6,1.4,0.2,Iris-setosa 6 | 5.4,3.9,1.7,0.4,Iris-setosa 7 | 4.6,3.4,1.4,0.3,Iris-setosa 8 | 5.0,3.4,1.5,0.2,Iris-setosa 9 | 4.4,2.9,1.4,0.2,Iris-setosa 10 | 4.9,3.1,1.5,0.1,Iris-setosa 11 | 5.4,3.7,1.5,0.2,Iris-setosa 12 | 4.8,3.4,1.6,0.2,Iris-setosa 13 | 4.8,3.0,1.4,0.1,Iris-setosa 14 | 4.3,3.0,1.1,0.1,Iris-setosa 15 | 5.8,4.0,1.2,0.2,Iris-setosa 16 | 5.7,4.4,1.5,0.4,Iris-setosa 17 | 5.4,3.9,1.3,0.4,Iris-setosa 18 | 5.1,3.5,1.4,0.3,Iris-setosa 19 | 5.7,3.8,1.7,0.3,Iris-setosa 20 | 5.1,3.8,1.5,0.3,Iris-setosa 21 | 5.4,3.4,1.7,0.2,Iris-setosa 22 | 5.1,3.7,1.5,0.4,Iris-setosa 23 | 4.6,3.6,1.0,0.2,Iris-setosa 24 | 5.1,3.3,1.7,0.5,Iris-setosa 25 | 4.8,3.4,1.9,0.2,Iris-setosa 26 | 5.0,3.0,1.6,0.2,Iris-setosa 27 | 5.0,3.4,1.6,0.4,Iris-setosa 28 | 5.2,3.5,1.5,0.2,Iris-setosa 29 | 5.2,3.4,1.4,0.2,Iris-setosa 30 | 4.7,3.2,1.6,0.2,Iris-setosa 31 | 4.8,3.1,1.6,0.2,Iris-setosa 32 | 5.4,3.4,1.5,0.4,Iris-setosa 33 | 5.2,4.1,1.5,0.1,Iris-setosa 34 | 5.5,4.2,1.4,0.2,Iris-setosa 35 | 4.9,3.1,1.5,0.1,Iris-setosa 36 | 5.0,3.2,1.2,0.2,Iris-setosa 37 | 5.5,3.5,1.3,0.2,Iris-setosa 38 | 4.9,3.1,1.5,0.1,Iris-setosa 39 | 4.4,3.0,1.3,0.2,Iris-setosa 40 | 5.1,3.4,1.5,0.2,Iris-setosa 41 | 5.0,3.5,1.3,0.3,Iris-setosa 42 | 4.5,2.3,1.3,0.3,Iris-setosa 43 | 4.4,3.2,1.3,0.2,Iris-setosa 44 | 5.0,3.5,1.6,0.6,Iris-setosa 45 | 5.1,3.8,1.9,0.4,Iris-setosa 46 | 4.8,3.0,1.4,0.3,Iris-setosa 47 | 5.1,3.8,1.6,0.2,Iris-setosa 48 | 4.6,3.2,1.4,0.2,Iris-setosa 49 | 5.3,3.7,1.5,0.2,Iris-setosa 50 | 5.0,3.3,1.4,0.2,Iris-setosa 51 | 7.0,3.2,4.7,1.4,Iris-versicolor 52 | 6.4,3.2,4.5,1.5,Iris-versicolor 53 | 6.9,3.1,4.9,1.5,Iris-versicolor 54 | 5.5,2.3,4.0,1.3,Iris-versicolor 55 | 6.5,2.8,4.6,1.5,Iris-versicolor 56 | 5.7,2.8,4.5,1.3,Iris-versicolor 57 | 6.3,3.3,4.7,1.6,Iris-versicolor 58 | 4.9,2.4,3.3,1.0,Iris-versicolor 59 | 6.6,2.9,4.6,1.3,Iris-versicolor 60 | 5.2,2.7,3.9,1.4,Iris-versicolor 61 | 5.0,2.0,3.5,1.0,Iris-versicolor 62 | 5.9,3.0,4.2,1.5,Iris-versicolor 63 | 6.0,2.2,4.0,1.0,Iris-versicolor 64 | 6.1,2.9,4.7,1.4,Iris-versicolor 65 | 5.6,2.9,3.6,1.3,Iris-versicolor 66 | 6.7,3.1,4.4,1.4,Iris-versicolor 67 | 5.6,3.0,4.5,1.5,Iris-versicolor 68 | 5.8,2.7,4.1,1.0,Iris-versicolor 69 | 6.2,2.2,4.5,1.5,Iris-versicolor 70 | 5.6,2.5,3.9,1.1,Iris-versicolor 71 | 5.9,3.2,4.8,1.8,Iris-versicolor 72 | 6.1,2.8,4.0,1.3,Iris-versicolor 73 | 6.3,2.5,4.9,1.5,Iris-versicolor 74 | 6.1,2.8,4.7,1.2,Iris-versicolor 75 | 6.4,2.9,4.3,1.3,Iris-versicolor 76 | 6.6,3.0,4.4,1.4,Iris-versicolor 77 | 6.8,2.8,4.8,1.4,Iris-versicolor 78 | 6.7,3.0,5.0,1.7,Iris-versicolor 79 | 6.0,2.9,4.5,1.5,Iris-versicolor 80 | 5.7,2.6,3.5,1.0,Iris-versicolor 81 | 5.5,2.4,3.8,1.1,Iris-versicolor 82 | 5.5,2.4,3.7,1.0,Iris-versicolor 83 | 5.8,2.7,3.9,1.2,Iris-versicolor 84 | 6.0,2.7,5.1,1.6,Iris-versicolor 85 | 5.4,3.0,4.5,1.5,Iris-versicolor 86 | 6.0,3.4,4.5,1.6,Iris-versicolor 87 | 6.7,3.1,4.7,1.5,Iris-versicolor 88 | 6.3,2.3,4.4,1.3,Iris-versicolor 89 | 5.6,3.0,4.1,1.3,Iris-versicolor 90 | 5.5,2.5,4.0,1.3,Iris-versicolor 91 | 5.5,2.6,4.4,1.2,Iris-versicolor 92 | 6.1,3.0,4.6,1.4,Iris-versicolor 93 | 5.8,2.6,4.0,1.2,Iris-versicolor 94 | 5.0,2.3,3.3,1.0,Iris-versicolor 95 | 5.6,2.7,4.2,1.3,Iris-versicolor 96 | 5.7,3.0,4.2,1.2,Iris-versicolor 97 | 5.7,2.9,4.2,1.3,Iris-versicolor 98 | 6.2,2.9,4.3,1.3,Iris-versicolor 99 | 5.1,2.5,3.0,1.1,Iris-versicolor 100 | 5.7,2.8,4.1,1.3,Iris-versicolor 101 | 6.3,3.3,6.0,2.5,Iris-virginica 102 | 5.8,2.7,5.1,1.9,Iris-virginica 103 | 7.1,3.0,5.9,2.1,Iris-virginica 104 | 6.3,2.9,5.6,1.8,Iris-virginica 105 | 6.5,3.0,5.8,2.2,Iris-virginica 106 | 7.6,3.0,6.6,2.1,Iris-virginica 107 | 4.9,2.5,4.5,1.7,Iris-virginica 108 | 7.3,2.9,6.3,1.8,Iris-virginica 109 | 6.7,2.5,5.8,1.8,Iris-virginica 110 | 7.2,3.6,6.1,2.5,Iris-virginica 111 | 6.5,3.2,5.1,2.0,Iris-virginica 112 | 6.4,2.7,5.3,1.9,Iris-virginica 113 | 6.8,3.0,5.5,2.1,Iris-virginica 114 | 5.7,2.5,5.0,2.0,Iris-virginica 115 | 5.8,2.8,5.1,2.4,Iris-virginica 116 | 6.4,3.2,5.3,2.3,Iris-virginica 117 | 6.5,3.0,5.5,1.8,Iris-virginica 118 | 7.7,3.8,6.7,2.2,Iris-virginica 119 | 7.7,2.6,6.9,2.3,Iris-virginica 120 | 6.0,2.2,5.0,1.5,Iris-virginica 121 | 6.9,3.2,5.7,2.3,Iris-virginica 122 | 5.6,2.8,4.9,2.0,Iris-virginica 123 | 7.7,2.8,6.7,2.0,Iris-virginica 124 | 6.3,2.7,4.9,1.8,Iris-virginica 125 | 6.7,3.3,5.7,2.1,Iris-virginica 126 | 7.2,3.2,6.0,1.8,Iris-virginica 127 | 6.2,2.8,4.8,1.8,Iris-virginica 128 | 6.1,3.0,4.9,1.8,Iris-virginica 129 | 6.4,2.8,5.6,2.1,Iris-virginica 130 | 7.2,3.0,5.8,1.6,Iris-virginica 131 | 7.4,2.8,6.1,1.9,Iris-virginica 132 | 7.9,3.8,6.4,2.0,Iris-virginica 133 | 6.4,2.8,5.6,2.2,Iris-virginica 134 | 6.3,2.8,5.1,1.5,Iris-virginica 135 | 6.1,2.6,5.6,1.4,Iris-virginica 136 | 7.7,3.0,6.1,2.3,Iris-virginica 137 | 6.3,3.4,5.6,2.4,Iris-virginica 138 | 6.4,3.1,5.5,1.8,Iris-virginica 139 | 6.0,3.0,4.8,1.8,Iris-virginica 140 | 6.9,3.1,5.4,2.1,Iris-virginica 141 | 6.7,3.1,5.6,2.4,Iris-virginica 142 | 6.9,3.1,5.1,2.3,Iris-virginica 143 | 5.8,2.7,5.1,1.9,Iris-virginica 144 | 6.8,3.2,5.9,2.3,Iris-virginica 145 | 6.7,3.3,5.7,2.5,Iris-virginica 146 | 6.7,3.0,5.2,2.3,Iris-virginica 147 | 6.3,2.5,5.0,1.9,Iris-virginica 148 | 6.5,3.0,5.2,2.0,Iris-virginica 149 | 6.2,3.4,5.4,2.3,Iris-virginica 150 | 5.9,3.0,5.1,1.8,Iris-virginica 151 | 152 | -------------------------------------------------------------------------------- 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1469974824,sue,advanced.html,31,, 24 | 1469974854,sue,advanced.html,32,, 25 | 1469974924,sue,advanced.html,33,, 26 | 1469977424,bob,intro.html,1,TRUE,10 27 | 1469977454,bob,intro.html,1,, 28 | 1469977484,bob,intro.html,1,, 29 | 1469977634,bob,intro.html,1,, 30 | 1469977664,bob,intro.html,1,, 31 | 1469974634,cheryl,intro.html,12,, 32 | 1469974664,cheryl,intro.html,13,, 33 | 1469977694,bob,intro.html,1,, 34 | 1469977724,bob,intro.html,1,, 35 | -------------------------------------------------------------------------------- /notebooks/Prerequisites/datasets/men2004.csv: -------------------------------------------------------------------------------- 1 | Athlete,Medals 2 | "PHELPS, Michael",8 3 | "THORPE, Ian",4 4 | "SCHOEMAN, Roland",3 5 | "PEIRSOL, Aaron",3 6 | "CROCKER, Ian",3 7 | "KITAJIMA, Kosuke",3 8 | "HANSEN, Brendan",3 9 | "VAN DEN HOOGENBAND, Pieter",3 10 | "HACKETT, Grant",3 11 | "MORITA, Tomomi",2 12 | "LEZAK, Jason",2 13 | "ROGAN, Markus",2 14 | "KELLER, Klete",2 15 | "HALL, Gary Jr.",2 16 | "LOCHTE, Ryan",2 17 | "WALKER, Neil",2 18 | "YAMAMOTO, Takashi",2 19 | "SPRENGER, Nicholas",1 20 | "OKUMURA, Yoshihiro",1 21 | "PARRY, Stephen",1 22 | "PEARSON, Todd",1 23 | "ZASTROW, Mitja",1 24 | "PELLICIARI, Matteo",1 25 | "WOODWARD, Gabe",1 26 | "SERDINOV, Andriy",1 27 | "VENDT, Erik",1 28 | "ROSOLINO, Massimiliano",1 29 | "VEENS, Mark Hermanus",1 30 | "RUPPRATH, Thomas",1 31 | "VANDERKAAY, Peter",1 32 | "TOWNSEND, Darian",1 33 | "STEVENS, Craig",1 34 | "NEETHLING, Ryk",1 35 | "BOVELL, George",1 36 | "MEEUW, Helge",1 37 | "FLOREA, Razvan Ionut",1 38 | "CAPPELLAZZO, Federico",1 39 | "CERCATO, Simone",1 40 | "CONRAD, Lars",1 41 | "CSEH, Laszlo",1 42 | "DAVIES, David",1 43 | "DRAGANJA, Duje",1 44 | "DRIESEN, Steffen",1 45 | "DUBOSCQ, Hugues",1 46 | "DUSING, Nate",1 47 | "FERNS, Lyndon",1 48 | "GANGLOFF, Mark",1 49 | "MATKOVICH, Antony",1 50 | "GOLDBLATT, Scott",1 51 | "GYURTA, Daniel",1 52 | "JENSEN, Larsen",1 53 | "KENKHUIS, Johan",1 54 | "KETCHUM, Dan",1 55 | "KLIM, Michael",1 56 | "KRAYZELBURG, Lenny",1 57 | "KRUPPA, Jens",1 58 | "BREMBILLA, Emiliano",1 59 | "MAGNINI, Filippo",1 60 | "ZWERING, Klaas-Erik",1 61 | -------------------------------------------------------------------------------- /notebooks/Prerequisites/datasets/men2008.csv: -------------------------------------------------------------------------------- 1 | Athlete,Medals 2 | "PHELPS, Michael",8 3 | "LOCHTE, Ryan",4 4 | "BERNARD, Alain",3 5 | "SULLIVAN, Eamon",3 6 | "LAUTERSTEIN, Andrew",3 7 | "GREVERS, Matt",3 8 | "LEZAK, Jason",3 9 | "CSEH, Laszlo",3 10 | "KITAJIMA, Kosuke",3 11 | "PEIRSOL, Aaron",3 12 | "RICKARD, Brenton",2 13 | "LEVEAUX, Amaury",2 14 | "HACKETT, Grant",2 15 | "STOECKEL, Hayden",2 16 | "TARGETT, Matt",2 17 | "DUBOSCQ, Hugues",2 18 | "VANDERKAAY, Peter",2 19 | "PARK, Taehwan",2 20 | "CIELO FILHO, Cesar",2 21 | "VYATCHANIN, Arkady",2 22 | "BRODIE, Leith",2 23 | "WEBER-GALE, Garrett",2 24 | "MURPHY, Patrick",2 25 | "OEN, Alexander Dale",1 26 | "PALMER, Kirk",1 27 | "ADRIAN, Nathan",1 28 | "PINE, Adam",1 29 | "POLISHCHUK, Mikhail",1 30 | "SATO, Hisayoshi",1 31 | "SPRENGER, Christian",1 32 | "MIYASHITA, Junichi",1 33 | "SUKHORUKOV, Alexander",1 34 | "VAN DER WEIJDEN, Maarten",1 35 | "VENDT, Erik",1 36 | "WALTERS, David",1 37 | "WILDMAN-TOBRINER, Ben",1 38 | "STEIMETZ, Boris",1 39 | "LOBINTSEV, Nikita",1 40 | "MELLOULI, Oussama",1 41 | "MATSUDA, Takeshi",1 42 | "BOUSQUET, Frederick",1 43 | "BRITS, Grant",1 44 | "CALLUS, Ashley",1 45 | "CAVIC, Milorad",1 46 | "COCHRANE, Ryan",1 47 | "CROCKER, Ian",1 48 | "DAVIES, David",1 49 | "DELANEY, Ashley",1 50 | "FFROST, Nick",1 51 | "FUJII, Takuro",1 52 | "GANGLOFF, Mark",1 53 | "GILOT, Fabien",1 54 | "HANSEN, Brendan",1 55 | "IZOTOV, Danila",1 56 | "JENSEN, Larsen",1 57 | "JONES, Cullen",1 58 | "KELLER, Klete",1 59 | "LAGUNOV, Evgeniy",1 60 | "BERENS, Ricky",1 61 | "LURZ, Thomas",1 62 | "MALLET, Gregory",1 63 | "ZHANG, Lin",1 64 | -------------------------------------------------------------------------------- /notebooks/Prerequisites/datasets/olympics.csv: -------------------------------------------------------------------------------- 1 | 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 2 | ,№ Summer,01 !,02 !,03 !,Total,№ Winter,01 !,02 !,03 !,Total,№ Games,01 !,02 !,03 !,Combined total 3 | Afghanistan (AFG),13,0,0,2,2,0,0,0,0,0,13,0,0,2,2 4 | Algeria (ALG),12,5,2,8,15,3,0,0,0,0,15,5,2,8,15 5 | Argentina (ARG),23,18,24,28,70,18,0,0,0,0,41,18,24,28,70 6 | Armenia (ARM),5,1,2,9,12,6,0,0,0,0,11,1,2,9,12 7 | Australasia (ANZ) [ANZ],2,3,4,5,12,0,0,0,0,0,2,3,4,5,12 8 | Australia (AUS) [AUS] [Z],25,139,152,177,468,18,5,3,4,12,43,144,155,181,480 9 | Austria (AUT),26,18,33,35,86,22,59,78,81,218,48,77,111,116,304 10 | Azerbaijan (AZE),5,6,5,15,26,5,0,0,0,0,10,6,5,15,26 11 | Bahamas (BAH),15,5,2,5,12,0,0,0,0,0,15,5,2,5,12 12 | Bahrain (BRN),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1 13 | Barbados (BAR) [BAR],11,0,0,1,1,0,0,0,0,0,11,0,0,1,1 14 | Belarus (BLR),5,12,24,39,75,6,6,4,5,15,11,18,28,44,90 15 | Belgium (BEL),25,37,52,53,142,20,1,1,3,5,45,38,53,56,147 16 | Bermuda (BER),17,0,0,1,1,7,0,0,0,0,24,0,0,1,1 17 | Bohemia (BOH) [BOH] [Z],3,0,1,3,4,0,0,0,0,0,3,0,1,3,4 18 | Botswana (BOT),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1 19 | Brazil (BRA),21,23,30,55,108,7,0,0,0,0,28,23,30,55,108 20 | British West Indies (BWI) [BWI],1,0,0,2,2,0,0,0,0,0,1,0,0,2,2 21 | Bulgaria (BUL) [H],19,51,85,78,214,19,1,2,3,6,38,52,87,81,220 22 | Burundi (BDI),5,1,0,0,1,0,0,0,0,0,5,1,0,0,1 23 | Cameroon (CMR),13,3,1,1,5,1,0,0,0,0,14,3,1,1,5 24 | Canada (CAN),25,59,99,121,279,22,62,56,52,170,47,121,155,173,449 25 | Chile (CHI) [I],22,2,7,4,13,16,0,0,0,0,38,2,7,4,13 26 | China (CHN) [CHN],9,201,146,126,473,10,12,22,19,53,19,213,168,145,526 27 | Colombia (COL),18,2,6,11,19,1,0,0,0,0,19,2,6,11,19 28 | Costa Rica (CRC),14,1,1,2,4,6,0,0,0,0,20,1,1,2,4 29 | Ivory Coast (CIV) [CIV],12,0,1,0,1,0,0,0,0,0,12,0,1,0,1 30 | Croatia (CRO),6,6,7,10,23,7,4,6,1,11,13,10,13,11,34 31 | Cuba (CUB) [Z],19,72,67,70,209,0,0,0,0,0,19,72,67,70,209 32 | Cyprus (CYP),9,0,1,0,1,10,0,0,0,0,19,0,1,0,1 33 | Czech Republic (CZE) [CZE],5,14,15,15,44,6,7,9,8,24,11,21,24,23,68 34 | Czechoslovakia (TCH) [TCH],16,49,49,45,143,16,2,8,15,25,32,51,57,60,168 35 | Denmark (DEN) [Z],26,43,68,68,179,13,0,1,0,1,39,43,69,68,180 36 | Djibouti (DJI) [B],7,0,0,1,1,0,0,0,0,0,7,0,0,1,1 37 | Dominican Republic (DOM),13,3,2,1,6,0,0,0,0,0,13,3,2,1,6 38 | Ecuador (ECU),13,1,1,0,2,0,0,0,0,0,13,1,1,0,2 39 | Egypt (EGY) [EGY] [Z],21,7,9,10,26,1,0,0,0,0,22,7,9,10,26 40 | Eritrea (ERI),4,0,0,1,1,0,0,0,0,0,4,0,0,1,1 41 | Estonia (EST),11,9,9,15,33,9,4,2,1,7,20,13,11,16,40 42 | Ethiopia (ETH),12,21,7,17,45,2,0,0,0,0,14,21,7,17,45 43 | Finland (FIN),24,101,84,117,302,22,42,62,57,161,46,143,146,174,463 44 | France (FRA) [O] [P] [Z],27,202,223,246,671,22,31,31,47,109,49,233,254,293,780 45 | Gabon (GAB),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1 46 | Georgia (GEO),5,6,5,14,25,6,0,0,0,0,11,6,5,14,25 47 | Germany (GER) [GER] [Z],15,174,182,217,573,11,78,78,53,209,26,252,260,270,782 48 | United Team of Germany (EUA) [EUA],3,28,54,36,118,3,8,6,5,19,6,36,60,41,137 49 | East Germany (GDR) [GDR],5,153,129,127,409,6,39,36,35,110,11,192,165,162,519 50 | West Germany (FRG) [FRG],5,56,67,81,204,6,11,15,13,39,11,67,82,94,243 51 | Ghana (GHA) [GHA],13,0,1,3,4,1,0,0,0,0,14,0,1,3,4 52 | Great Britain (GBR) [GBR] [Z],27,236,272,272,780,22,10,4,12,26,49,246,276,284,806 53 | Greece (GRE) [Z],27,30,42,39,111,18,0,0,0,0,45,30,42,39,111 54 | Grenada (GRN),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1 55 | Guatemala (GUA),13,0,1,0,1,1,0,0,0,0,14,0,1,0,1 56 | Guyana (GUY) [GUY],16,0,0,1,1,0,0,0,0,0,16,0,0,1,1 57 | Haiti (HAI) [J],14,0,1,1,2,0,0,0,0,0,14,0,1,1,2 58 | Hong Kong (HKG) [HKG],15,1,1,1,3,4,0,0,0,0,19,1,1,1,3 59 | Hungary (HUN),25,167,144,165,476,22,0,2,4,6,47,167,146,169,482 60 | Iceland (ISL),19,0,2,2,4,17,0,0,0,0,36,0,2,2,4 61 | India (IND) [F],23,9,6,11,26,9,0,0,0,0,32,9,6,11,26 62 | Indonesia (INA),14,6,10,11,27,0,0,0,0,0,14,6,10,11,27 63 | Iran (IRI) [K],15,15,20,25,60,10,0,0,0,0,25,15,20,25,60 64 | Iraq (IRQ),13,0,0,1,1,0,0,0,0,0,13,0,0,1,1 65 | Ireland (IRL),20,9,8,12,29,6,0,0,0,0,26,9,8,12,29 66 | Israel (ISR),15,1,1,5,7,6,0,0,0,0,21,1,1,5,7 67 | Italy (ITA) [M] [S],26,198,166,185,549,22,37,34,43,114,48,235,200,228,663 68 | Jamaica (JAM) [JAM],16,17,30,20,67,7,0,0,0,0,23,17,30,20,67 69 | Japan (JPN),21,130,126,142,398,20,10,17,18,45,41,140,143,160,443 70 | Kazakhstan (KAZ),5,16,17,19,52,6,1,3,3,7,11,17,20,22,59 71 | Kenya (KEN),13,25,32,29,86,3,0,0,0,0,16,25,32,29,86 72 | North Korea (PRK),9,14,12,21,47,8,0,1,1,2,17,14,13,22,49 73 | South Korea (KOR),16,81,82,80,243,17,26,17,10,53,33,107,99,90,296 74 | Kuwait (KUW),12,0,0,2,2,0,0,0,0,0,12,0,0,2,2 75 | Kyrgyzstan (KGZ),5,0,1,2,3,6,0,0,0,0,11,0,1,2,3 76 | Latvia (LAT),10,3,11,5,19,10,0,4,3,7,20,3,15,8,26 77 | Lebanon (LIB),16,0,2,2,4,16,0,0,0,0,32,0,2,2,4 78 | Liechtenstein (LIE),16,0,0,0,0,18,2,2,5,9,34,2,2,5,9 79 | Lithuania (LTU),8,6,5,10,21,8,0,0,0,0,16,6,5,10,21 80 | Luxembourg (LUX) [O],22,1,1,0,2,8,0,2,0,2,30,1,3,0,4 81 | Macedonia (MKD),5,0,0,1,1,5,0,0,0,0,10,0,0,1,1 82 | Malaysia (MAS) [MAS],12,0,3,3,6,0,0,0,0,0,12,0,3,3,6 83 | Mauritius (MRI),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1 84 | Mexico (MEX),22,13,21,28,62,8,0,0,0,0,30,13,21,28,62 85 | Moldova (MDA),5,0,2,5,7,6,0,0,0,0,11,0,2,5,7 86 | Mongolia (MGL),12,2,9,13,24,13,0,0,0,0,25,2,9,13,24 87 | Montenegro (MNE),2,0,1,0,1,2,0,0,0,0,4,0,1,0,1 88 | Morocco (MAR),13,6,5,11,22,6,0,0,0,0,19,6,5,11,22 89 | Mozambique (MOZ),9,1,0,1,2,0,0,0,0,0,9,1,0,1,2 90 | Namibia (NAM),6,0,4,0,4,0,0,0,0,0,6,0,4,0,4 91 | Netherlands (NED) [Z],25,77,85,104,266,20,37,38,35,110,45,114,123,139,376 92 | Netherlands Antilles (AHO) [AHO] [I],13,0,1,0,1,2,0,0,0,0,15,0,1,0,1 93 | New Zealand (NZL) [NZL],22,42,18,39,99,15,0,1,0,1,37,42,19,39,100 94 | Niger (NIG),11,0,0,1,1,0,0,0,0,0,11,0,0,1,1 95 | Nigeria (NGR),15,3,8,12,23,0,0,0,0,0,15,3,8,12,23 96 | Norway (NOR) [Q],24,56,49,43,148,22,118,111,100,329,46,174,160,143,477 97 | Pakistan (PAK),16,3,3,4,10,2,0,0,0,0,18,3,3,4,10 98 | Panama (PAN),16,1,0,2,3,0,0,0,0,0,16,1,0,2,3 99 | Paraguay (PAR),11,0,1,0,1,1,0,0,0,0,12,0,1,0,1 100 | Peru (PER) [L],17,1,3,0,4,2,0,0,0,0,19,1,3,0,4 101 | Philippines (PHI),20,0,2,7,9,4,0,0,0,0,24,0,2,7,9 102 | Poland (POL),20,64,82,125,271,22,6,7,7,20,42,70,89,132,291 103 | Portugal (POR),23,4,8,11,23,7,0,0,0,0,30,4,8,11,23 104 | Puerto Rico (PUR),17,0,2,6,8,6,0,0,0,0,23,0,2,6,8 105 | Qatar (QAT),8,0,0,4,4,0,0,0,0,0,8,0,0,4,4 106 | Romania (ROU),20,88,94,119,301,20,0,0,1,1,40,88,94,120,302 107 | Russia (RUS) [RUS],5,132,121,142,395,6,49,40,35,124,11,181,161,177,519 108 | Russian Empire (RU1) [RU1],3,1,4,3,8,0,0,0,0,0,3,1,4,3,8 109 | Soviet Union (URS) [URS],9,395,319,296,1010,9,78,57,59,194,18,473,376,355,1204 110 | Unified Team (EUN) [EUN],1,45,38,29,112,1,9,6,8,23,2,54,44,37,135 111 | Saudi Arabia (KSA),10,0,1,2,3,0,0,0,0,0,10,0,1,2,3 112 | Senegal (SEN),13,0,1,0,1,5,0,0,0,0,18,0,1,0,1 113 | Serbia (SRB) [SRB],3,1,2,4,7,2,0,0,0,0,5,1,2,4,7 114 | Serbia and Montenegro (SCG) [SCG],3,2,4,3,9,3,0,0,0,0,6,2,4,3,9 115 | Singapore (SIN),15,0,2,2,4,0,0,0,0,0,15,0,2,2,4 116 | Slovakia (SVK) [SVK],5,7,9,8,24,6,2,2,1,5,11,9,11,9,29 117 | Slovenia (SLO),6,4,6,9,19,7,2,4,9,15,13,6,10,18,34 118 | South Africa (RSA),18,23,26,27,76,6,0,0,0,0,24,23,26,27,76 119 | Spain (ESP) [Z],22,37,59,35,131,19,1,0,1,2,41,38,59,36,133 120 | Sri Lanka (SRI) [SRI],16,0,2,0,2,0,0,0,0,0,16,0,2,0,2 121 | Sudan (SUD),11,0,1,0,1,0,0,0,0,0,11,0,1,0,1 122 | Suriname (SUR) [E],11,1,0,1,2,0,0,0,0,0,11,1,0,1,2 123 | Sweden (SWE) [Z],26,143,164,176,483,22,50,40,54,144,48,193,204,230,627 124 | Switzerland (SUI),27,47,73,65,185,22,50,40,48,138,49,97,113,113,323 125 | Syria (SYR),12,1,1,1,3,0,0,0,0,0,12,1,1,1,3 126 | Chinese Taipei (TPE) [TPE] [TPE2],13,2,7,12,21,11,0,0,0,0,24,2,7,12,21 127 | Tajikistan (TJK),5,0,1,2,3,4,0,0,0,0,9,0,1,2,3 128 | Tanzania (TAN) [TAN],12,0,2,0,2,0,0,0,0,0,12,0,2,0,2 129 | Thailand (THA),15,7,6,11,24,3,0,0,0,0,18,7,6,11,24 130 | Togo (TOG),9,0,0,1,1,1,0,0,0,0,10,0,0,1,1 131 | Tonga (TGA),8,0,1,0,1,1,0,0,0,0,9,0,1,0,1 132 | Trinidad and Tobago (TRI) [TRI],16,2,5,11,18,3,0,0,0,0,19,2,5,11,18 133 | Tunisia (TUN),13,3,3,4,10,0,0,0,0,0,13,3,3,4,10 134 | Turkey (TUR),21,39,25,24,88,16,0,0,0,0,37,39,25,24,88 135 | Uganda (UGA),14,2,3,2,7,0,0,0,0,0,14,2,3,2,7 136 | Ukraine (UKR),5,33,27,55,115,6,2,1,4,7,11,35,28,59,122 137 | United Arab Emirates (UAE),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1 138 | United States (USA) [P] [Q] [R] [Z],26,976,757,666,2399,22,96,102,84,282,48,1072,859,750,2681 139 | Uruguay (URU),20,2,2,6,10,1,0,0,0,0,21,2,2,6,10 140 | Uzbekistan (UZB),5,5,5,10,20,6,1,0,0,1,11,6,5,10,21 141 | Venezuela (VEN),17,2,2,8,12,4,0,0,0,0,21,2,2,8,12 142 | Vietnam (VIE),14,0,2,0,2,0,0,0,0,0,14,0,2,0,2 143 | Virgin Islands (ISV),11,0,1,0,1,7,0,0,0,0,18,0,1,0,1 144 | Yugoslavia (YUG) [YUG],16,26,29,28,83,14,0,3,1,4,30,26,32,29,87 145 | Independent Olympic Participants (IOP) [IOP],1,0,1,2,3,0,0,0,0,0,1,0,1,2,3 146 | Zambia (ZAM) [ZAM],12,0,1,1,2,0,0,0,0,0,12,0,1,1,2 147 | Zimbabwe (ZIM) [ZIM],12,3,4,1,8,1,0,0,0,0,13,3,4,1,8 148 | Mixed team (ZZX) [ZZX],3,8,5,4,17,0,0,0,0,0,3,8,5,4,17 149 | Totals,27,4809,4775,5130,14714,22,959,958,948,2865,49,5768,5733,6078,17579 150 | -------------------------------------------------------------------------------- /notebooks/Prerequisites/datasets/tips.csv: -------------------------------------------------------------------------------- 1 | "total_bill","tip","sex","smoker","day","time","size" 2 | 16.99,1.01,"Female","No","Sun","Dinner",2 3 | 10.34,1.66,"Male","No","Sun","Dinner",3 4 | 21.01,3.5,"Male","No","Sun","Dinner",3 5 | 23.68,3.31,"Male","No","Sun","Dinner",2 6 | 24.59,3.61,"Female","No","Sun","Dinner",4 7 | 25.29,4.71,"Male","No","Sun","Dinner",4 8 | 8.77,2,"Male","No","Sun","Dinner",2 9 | 26.88,3.12,"Male","No","Sun","Dinner",4 10 | 15.04,1.96,"Male","No","Sun","Dinner",2 11 | 14.78,3.23,"Male","No","Sun","Dinner",2 12 | 10.27,1.71,"Male","No","Sun","Dinner",2 13 | 35.26,5,"Female","No","Sun","Dinner",4 14 | 15.42,1.57,"Male","No","Sun","Dinner",2 15 | 18.43,3,"Male","No","Sun","Dinner",4 16 | 14.83,3.02,"Female","No","Sun","Dinner",2 17 | 21.58,3.92,"Male","No","Sun","Dinner",2 18 | 10.33,1.67,"Female","No","Sun","Dinner",3 19 | 16.29,3.71,"Male","No","Sun","Dinner",3 20 | 16.97,3.5,"Female","No","Sun","Dinner",3 21 | 20.65,3.35,"Male","No","Sat","Dinner",3 22 | 17.92,4.08,"Male","No","Sat","Dinner",2 23 | 20.29,2.75,"Female","No","Sat","Dinner",2 24 | 15.77,2.23,"Female","No","Sat","Dinner",2 25 | 39.42,7.58,"Male","No","Sat","Dinner",4 26 | 19.82,3.18,"Male","No","Sat","Dinner",2 27 | 17.81,2.34,"Male","No","Sat","Dinner",4 28 | 13.37,2,"Male","No","Sat","Dinner",2 29 | 12.69,2,"Male","No","Sat","Dinner",2 30 | 21.7,4.3,"Male","No","Sat","Dinner",2 31 | 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16,2,"Male","Yes","Thur","Lunch",2 141 | 13.16,2.75,"Female","No","Thur","Lunch",2 142 | 17.47,3.5,"Female","No","Thur","Lunch",2 143 | 34.3,6.7,"Male","No","Thur","Lunch",6 144 | 41.19,5,"Male","No","Thur","Lunch",5 145 | 27.05,5,"Female","No","Thur","Lunch",6 146 | 16.43,2.3,"Female","No","Thur","Lunch",2 147 | 8.35,1.5,"Female","No","Thur","Lunch",2 148 | 18.64,1.36,"Female","No","Thur","Lunch",3 149 | 11.87,1.63,"Female","No","Thur","Lunch",2 150 | 9.78,1.73,"Male","No","Thur","Lunch",2 151 | 7.51,2,"Male","No","Thur","Lunch",2 152 | 14.07,2.5,"Male","No","Sun","Dinner",2 153 | 13.13,2,"Male","No","Sun","Dinner",2 154 | 17.26,2.74,"Male","No","Sun","Dinner",3 155 | 24.55,2,"Male","No","Sun","Dinner",4 156 | 19.77,2,"Male","No","Sun","Dinner",4 157 | 29.85,5.14,"Female","No","Sun","Dinner",5 158 | 48.17,5,"Male","No","Sun","Dinner",6 159 | 25,3.75,"Female","No","Sun","Dinner",4 160 | 13.39,2.61,"Female","No","Sun","Dinner",2 161 | 16.49,2,"Male","No","Sun","Dinner",4 162 | 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20.53,4,"Male","Yes","Thur","Lunch",4 207 | 16.47,3.23,"Female","Yes","Thur","Lunch",3 208 | 26.59,3.41,"Male","Yes","Sat","Dinner",3 209 | 38.73,3,"Male","Yes","Sat","Dinner",4 210 | 24.27,2.03,"Male","Yes","Sat","Dinner",2 211 | 12.76,2.23,"Female","Yes","Sat","Dinner",2 212 | 30.06,2,"Male","Yes","Sat","Dinner",3 213 | 25.89,5.16,"Male","Yes","Sat","Dinner",4 214 | 48.33,9,"Male","No","Sat","Dinner",4 215 | 13.27,2.5,"Female","Yes","Sat","Dinner",2 216 | 28.17,6.5,"Female","Yes","Sat","Dinner",3 217 | 12.9,1.1,"Female","Yes","Sat","Dinner",2 218 | 28.15,3,"Male","Yes","Sat","Dinner",5 219 | 11.59,1.5,"Male","Yes","Sat","Dinner",2 220 | 7.74,1.44,"Male","Yes","Sat","Dinner",2 221 | 30.14,3.09,"Female","Yes","Sat","Dinner",4 222 | 12.16,2.2,"Male","Yes","Fri","Lunch",2 223 | 13.42,3.48,"Female","Yes","Fri","Lunch",2 224 | 8.58,1.92,"Male","Yes","Fri","Lunch",1 225 | 15.98,3,"Female","No","Fri","Lunch",3 226 | 13.42,1.58,"Male","Yes","Fri","Lunch",2 227 | 16.27,2.5,"Female","Yes","Fri","Lunch",2 228 | 10.09,2,"Female","Yes","Fri","Lunch",2 229 | 20.45,3,"Male","No","Sat","Dinner",4 230 | 13.28,2.72,"Male","No","Sat","Dinner",2 231 | 22.12,2.88,"Female","Yes","Sat","Dinner",2 232 | 24.01,2,"Male","Yes","Sat","Dinner",4 233 | 15.69,3,"Male","Yes","Sat","Dinner",3 234 | 11.61,3.39,"Male","No","Sat","Dinner",2 235 | 10.77,1.47,"Male","No","Sat","Dinner",2 236 | 15.53,3,"Male","Yes","Sat","Dinner",2 237 | 10.07,1.25,"Male","No","Sat","Dinner",2 238 | 12.6,1,"Male","Yes","Sat","Dinner",2 239 | 32.83,1.17,"Male","Yes","Sat","Dinner",2 240 | 35.83,4.67,"Female","No","Sat","Dinner",3 241 | 29.03,5.92,"Male","No","Sat","Dinner",3 242 | 27.18,2,"Female","Yes","Sat","Dinner",2 243 | 22.67,2,"Male","Yes","Sat","Dinner",2 244 | 17.82,1.75,"Male","No","Sat","Dinner",2 245 | 18.78,3,"Female","No","Thur","Dinner",2 246 | -------------------------------------------------------------------------------- /notebooks/Regression/lr_images/gradient_descent.PNG: 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11 | 12 | 1st Assignment - Shahid Beheshti University 13 | 14 | February 26, 2023 15 | 16 | **Due date: April 3rd** 17 | 18 | ***\*\* You are required to write a detailed report for implementation tasks. \*\**** 19 | 20 | 1. Can gradient descent get stuck in a local minimum when training a logistic regression model? Why? 21 | 1. Suppose you are using polynomial regression. You plot the learning curves and you notice that there is a large gap between the training error and the validation error. What is happening? What are three ways to solve this? 22 | 1. Suppose you are using ridge regression and you notice that the training error and the validation error are almost equal and fairly high. Would you say that the model suffers from high bias or high variance? Should you increase the regularization hyperparameter α or reduce it? 23 | 1. Why would you want to use: 24 | - Ridge regression instead of plain linear regression (i.e., without any regularization)? 25 | - Lasso instead of ridge regression? 26 | - Elastic net instead of lasso regression? 27 | 5. Implement Linear Regression with **Mean Absolute Error** as the cost function from scratch. Compare your results with the Linear Regression module of **Scikit-Learn**. Apply the model on **[Diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)** 28 | 5. Implement Linear Regression using the normal equation as the training algorithm from scratch. Apply the model on **[Diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)** 29 | 5. Compare bootstrapping with cross-validation. In which conditions we should use bootstrapping? 30 | 5. Explain nested cross-validation and 5x2 cross-validation in detail and when we should use them.***(Extra Point)*** 31 | 9. How can we compare different models using statistical significance tests?***(Extra Point)*** 32 | 9. Implement Forward and Backward Feature selection algorithms from scratch with MSE as the metric. 33 | 9. Suppose the features in your training set have very different scales. Which algorithms *(Gradient Descent, Normal Equation, SVD)* might suffer from this, and how? What can you do about it? 34 | 9. In this part, you are going to work with the **[News Popularity Prediction** ](https://archive.ics.uci.edu/ml/datasets/online+news+popularity)**dataset. You will implement a regression model using the **Scikit-Learn** package to predict the *popularity of new articles (the number of times they will be shared online)* based on about 60 features. You are expected: 35 | - Perform exploratory data analysis on the dataset. 36 | - Propose 5 different hypothesis tests related to the dataset. At least use 3 different tests. 37 | - Try Ridge and Lasso regression. 38 | - Use various scaling methods and report their effects. 39 | - Add polynomial features and report their effect. 40 | - Try using GridSearchCV with RandomizedSearchCV to tune your model’s hyperparameters. ***(Extra Point)*** 41 | - Apply the feature selection methods that you have implemented in the above sections. 42 | - Get familiar with and implement the following loss functions from scratch and utilize them with a Linear Regression model and discuss their effect on the performance of the model. ***(Extra Point)*** 43 | - Absolute Error 44 | - Epsilon-sensetive error 45 | - Huber 46 | 13. Implement batch gradient descent with early stopping for *softmax regression* from scratch. Use it on a classification task on the [Penguins dataset](https://github.com/mwaskom/seaborn-data/blob/master/penguins.csv).***(Extra Point)*** 47 | 48 | **Good Luck!** 49 | -------------------------------------------------------------------------------- /pages/assignments/Assignment2.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: Assignment 2 4 | nav_order: 2 5 | has_children: false 6 | parent: Assignments 7 | permalink: /assignments/Assignment2 8 | --- 9 | 10 | **Machine Learning** 11 | 12 | 2nd Assignment - Shahid Beheshti University 13 | 14 | April 18, 2023 15 | 16 | **Due date: May 9th** 17 | 18 | ***\*\* You are required to write a detailed report for implementation tasks. \*\**** 19 | 20 | 1. Is it possible for an SVM classifier to provide a confidence score or probability when making predictions on a particular instance? Explain it. 21 | 1. What actions should you take if you have trained an SVM classifier using an RBF kernel but notice that it underfits the training set? Would it be appropriate to increase or decrease the value of γ (gamma) or C,or both? 22 | 1. What does it mean for a model to be ϵ-insensitive? 23 | 1. What is the difference between hard margin and soft margin SVM? When would you use each one? 24 | 1. Is a node’s Gini impurity generally lower or greater than its parent’s? Is it generally lower/greater,or always lower/greater? 25 | 1. Is it a good idea to consider scaling the input features if a Decision Tree underfits the training set? 26 | 1. How can you use tree-based models for feature selection? 27 | 1. How do you tweak the hyperparameters of the following model in mentioned circumstances: 28 | 1. AdaBoost - Underfitting 29 | 1. Gradient Boosting - Overfitting 30 | 1. What is the difference between homogeneous and heterogeneous ensembles? Which one is more powerful? 31 | 1. How ROC and AUC are being used in the evaluation of classification performance? 32 | 1. How does the threshold value used in classification affect the model's performance? This value specifies a cut-off for an observation to be classified as either 0 or 1. Can you explain the trade-off between false positive and false negative rates, and how the choice of threshold value impacts precision and recall? 33 | 12. What is the difference between one-vs-one and one-vs-all multiclass classification approaches in classifiers? Under what circumstances would you use one over the other? 34 | 12. In this part, you are going to work with the [Vehicle Insurance Claim Fraud Detection ](https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection) dataset. You will implement multiple classification models using the Scikit-Learn package to predict if a claim application is fraudulent or not, based on about 32 features. You are expected: 35 | - Perform exploratory data analysis on the dataset. 36 | - Try to tackle the problem using the following models : 37 | - Logistic Regression 38 | - SVM 39 | - Decision-Trees 40 | - Random Forest 41 | - Other classifiers: KNN, Naive Bayes, Ensemble models **(Extra Point)** 42 | - Use stratified cross-validation to report your models' performance. 43 | - Check whether this dataset is imbalanced or not, if yes, try some techniques to overcome this issue. (including over-sampling, under-sampling, weight-based approaches, etc.) 44 | - Try to boost the performance of the SVM and Random Forest models that you have used in the above section by utilizing various methods (including hyperparameter tuning, different preprocessing methods, feature engineering, etc.). Don’t limit yourself only to the aforementioned methods, based on the quality of your work, extra scores may be granted. 45 | 14. How can you use SVM for anomaly detection? What are the challenges in using SVM for anomaly detection? **(Extra Point)** 46 | 14. Implement a Bagging classifier from scratch. You can use sklearn for the base model. Test your model on the [Penguins dataset. ](https://github.com/mwaskom/seaborn-data/blob/master/penguins.csv)**(Extra Point)** 47 | 14. How do you handle the class imbalance in Ensemble Learning? Provide some techniques and explain their working. **(Extra Point)** 48 | -------------------------------------------------------------------------------- /pages/assignments/Assignment3.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: Assignment 3 4 | nav_order: 3 5 | has_children: false 6 | parent: Assignments 7 | permalink: /assignments/Assignment3 8 | --- 9 | 10 | **Machine Learning** 11 | 12 | 3rd Assignment - Shahid Beheshti University 13 | 14 | May 12, 2023 15 | 16 | **Due date: May 31** 17 | 18 | ***\*\* You are required to write a detailed report for implementation tasks. \*\**** 19 | 20 | 1. What is the curse of dimensionality and how does it affect clustering? 21 | 1. In what cases would you use regular PCA, incremental PCA, randomized PCA, or random projection? 22 | 1. Does it make sense to chain two different dimensionality reduction algorithms? 23 | 1. What are the main assumptions and limitations of PCA? 24 | 1. How can clustering be used to improve the accuracy of the linear regression model? 25 | 1. How is entropy used as a clustering validation measure? 26 | 1. What is label propagation? Why would you implement it, and how? **(Extra Point)** 27 | 8. You are going to work on the [Supermarket dataset for predictive marketing](https://www.kaggle.com/datasets/hunter0007/ecommerce-dataset-for-predictive-marketing-2023) . Your task is to use clustering algorithms to segment the customers into distinct groups based on their shopping behavior and demographics. 28 | 29 | - Explore and preprocess the dataset. This may involve handling categorical variables and normalizing or scaling numerical features and feature engineering. 30 | - Use K-means clustering to identify the optimal number of clusters. Experiment with different values of K and use metrics such as the elbow method and silhouette score to evaluate the performance of the clustering. 31 | - Visualize the clusters and analyze their characteristics. This may involve plotting the clusters in 2D or 3D using PCA or t-SNE. 32 | - Experiment with other clustering algorithms such as DBSCAN or hierarchical clustering, and compare their performance with K-means. 33 | - Try to reduce data dimensionality using PCA before training your model, use different numbers of components and report their effects. **(Extra Point)** 34 | - Interpret the results and provide insights to the store owners. What are the distinct customer segments that have been identified? How can the store owners use this information to improve their marketing strategy, product offerings, or customer experience? **(Extra Point)** 35 | -------------------------------------------------------------------------------- /pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.001.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.001.jpeg -------------------------------------------------------------------------------- /pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.002.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.002.jpeg -------------------------------------------------------------------------------- /pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.003.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.003.png -------------------------------------------------------------------------------- /pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.004.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/pages/assignments/Final Project/Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.004.png -------------------------------------------------------------------------------- /pages/assignments/Final Project/ML Final Project.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: Final Project 4 | nav_order: 4 5 | has_children: false 6 | parent: Assignments 7 | permalink: /assignments/Final Project/ML Final Project 8 | --- 9 | 10 | **Machine Learning Final Project** 11 | 12 | ***Due date: July 1st*** 13 | 14 | The following documentation explains your Machine Learning Final Project which focuses on deploying a machine learning model on **Hugging Face Spaces** for a **movie recommendation task**. Students are required to complete the project **individually** or in **teams of two**, with no preference given to either option. 15 | 16 | **Movie Recommender Systems** 17 | 18 | Movie recommendation systems are an integral part of the entertainment industry, aiming to provide personalized movie suggestions to the users based on their preferences. The project entails analyzing a large dataset of movies, incorporating user preferences, and implementing algorithms to generate relevant movie recommendations. 19 | 20 | ![](Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.001.jpeg) 21 | 22 | **Dataset** 23 | 24 | The [Movies Dataset](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset) is a collection of data on 45,000 movies and contains information such as budget, revenue, release dates, languages, production countries, and companies for each movie. The dataset also includes ratings of 270,000 users for these movies. The data was obtained from The Movie Database (TMDb) API. 25 | 26 | **Method** 27 | 28 | You can employ a wide variety of methods and implement creative ideas you have to fulfill this objective. First, you need to get at least three movies from the user (along with other information based on your method), then you can employ one of the following approaches to implement your recommender system: 29 | 30 | 1. **Cluster-Based:** these methods are a popular approach in recommender systems that aim to group users or items into clusters based on their similarities. These clusters can then be used to make recommendations by leveraging the preferences or characteristics of other users or items within the same cluster. 31 | 32 | Recommendations can be generated by considering the clusters to which the input movies belong. Alternatively, movies from different clusters can be recommended to introduce new content to the user. Another option is to recommend movies that are most similar to the input movies or those located at the center of the clusters. Additionally, creative approaches can be used to generate recommendations. Extra scores may be granted for creative approaches. 33 | 34 | 2. **Recommendation-Specific Methods (Extra Point):** it includes collaborative filtering, content-based filtering, and hybrid methods. Collaborative filtering involves recommending items to users based on their similarity to other users' preferences. Content-based filtering, on the other hand, recommends items based on their similarity to the user's previously liked items. Hybrid methods combine both collaborative and content-based filtering approaches to provide more accurate and diverse recommendations. These methods can be implemented as part of your recommender system to enhance its performance and effectiveness. By leveraging these techniques, you can provide personalized and relevant recommendations to users based on their preferences and characteristics. *References for these methods are provided in the last part of the documentation for further reading.* 35 | 36 | **User Interface Design** 37 | 38 | ![](Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.002.jpeg) 39 | 40 | [Gradio ](https://gradio.app/)and [Streamlit](https://streamlit.io/) are two popular Python libraries used for designing user interfaces in machine learning projects. These libraries provide intuitive and customizable options for creating interactive interfaces that allow users to interact with machine learning models and visualize results. Both libraries seamlessly integrate with popular machine learning frameworks and offer versatile options for showcasing models and gathering user feedback. You need to employ one of these libraries to implement a user-friendly interface for your system. *References for these libraries are provided in the last part of the documentation for further reading.* 41 | 42 | ![](Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.003.png) 43 | 44 | **Model Deployment on HuggingFace** 45 | 46 | Machine learning model deployment on **HuggingFace Spaces** is a powerful way to make trained models readily available to users. HuggingFace Spaces provides a platform where developers and researchers can showcase their models, allowing others to easily interact with and benefit from their work. You need to deploy your final model and interface into HuggingFace spaces. *References for this section are provided in the last part of the documentation for further reading.* 47 | 48 | ![](Aspose.Words.7636a13b-df42-4639-941c-f366e156dae0.004.png) 49 | 50 | **Report** 51 | 52 | You are also required to write a detailed report on your implementation and the methods you have used. Explain the method that you have used, the recommender procedure and the logic behind it, and the similarity measures. Include pre-processing steps performed on the data and the explanation of the workflow for designing the interface and deploying to the HuggingFace spaces. At the end of the report, please include a list of all the additional tasks you have completed that were not mandatory but carried extra weight in terms of evaluation. 53 | 54 | **Extra points** 55 | 56 | - Extra scores may be granted for more attractive interfaces. 57 | - To enhance the user experience with recommended movies, you have the option to utilize the following APIs that provide additional information about movies, including movie posters. These APIs are compatible with the provided dataset. 58 | - [OMDb](https://www.omdbapi.com/) 59 | - [TMDB](https://www.themoviedb.org/) 60 | - Using different neural network architectures from plain MLP to more advanced ones like transformers to implement your model. You can also use graph-based recommendation systems, or reinforcement learning approaches in your method. 61 | - Evaluate the proposed method’s performance using specific metrics for recommendation systems. 62 | 63 | **Resources** 64 | 65 | - **Recommender systems methods:** 66 | 67 | [https://medium.com/@eskandar.sahel/recommender-systems-best-practices-a5962ae1b 560](https://medium.com/@eskandar.sahel/recommender-systems-best-practices-a5962ae1b560) 68 | 69 | 70 | 71 | 72 | 73 | [https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1- algorithms-evaluation-and-cold-start-6f696683d0ed ](https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed) 74 | 75 | [https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-2- deep-recommendation-sequence-prediction-automl-f134bc79d66b ](https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-2-deep-recommendation-sequence-prediction-automl-f134bc79d66b) 76 | 77 | [https://medium.com/berlin-tech-blog/deep-learning-for-recommender-systems-48c786a2 0e1a](https://medium.com/berlin-tech-blog/deep-learning-for-recommender-systems-48c786a20e1a) 78 | 79 | 80 | [https://heartbeat.comet.ml/recommender-systems-with-python-part-i-content-based-filteri ng-5df4940bd831 ](https://heartbeat.comet.ml/recommender-systems-with-python-part-i-content-based-filtering-5df4940bd831) 81 | 82 | [https://heartbeat.comet.ml/recommender-systems-with-python-part-ii-collaborative-filterin g-k-nearest-neighbors-algorithm-c8dcd5fd89b2 ](https://heartbeat.comet.ml/recommender-systems-with-python-part-ii-collaborative-filtering-k-nearest-neighbors-algorithm-c8dcd5fd89b2) 83 | 84 | [https://heartbeat.comet.ml/recommender-systems-with-python-part-iii-collaborative-filteri ng-singular-value-decomposition-5b5dcb3f242b](https://heartbeat.comet.ml/recommender-systems-with-python-part-iii-collaborative-filtering-singular-value-decomposition-5b5dcb3f242b) 85 | 86 | - **Course on Recommender systems** 87 | 88 | 89 | - **Evaluation metrics** 90 | 91 | 92 | 93 | - **User Interface Design** 94 | - **Gradio:** 95 | 96 | [https://ismailouahbi.medium.com/gradio-build-deploy-and-share-your-machine-le arning-models-7b38baba659c ](https://ismailouahbi.medium.com/gradio-build-deploy-and-share-your-machine-learning-models-7b38baba659c) 97 | 98 | [https://www.youtube.com/watch?v=RiCQzBluTxU ](https://www.youtube.com/watch?v=RiCQzBluTxU) 99 | 100 | 101 | - **Streamlit:** 102 | 103 | [https://www.justintodata.com/streamlit-python-tutorial/ ](https://www.justintodata.com/streamlit-python-tutorial/) 104 | 105 | [https://www.youtube.com/watch?v=43RJ3JByygE ](https://www.youtube.com/watch?v=43RJ3JByygE) 106 | 107 | 108 | - **Deployment on HuggingFace** 109 | 110 | [https://towardsdatascience.com/host-ml-apps-with-huggingface-spaces-7d5eef00e781 ](https://towardsdatascience.com/host-ml-apps-with-huggingface-spaces-7d5eef00e781) 111 | 112 | 113 | -------------------------------------------------------------------------------- /pages/assignments/assignments.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: Assignments 4 | nav_order: 3 5 | has_children: true 6 | permalink: /assignments 7 | --- 8 | 9 | # Assignments 10 | Each task should have its report and IPython Notebook. Once again, we emphasize the report; it should contain all your questions and your proper statistical answers. Use figures, pictures, and tables. 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Logistic regression (also called logit regression) is commonly used to estimate the probability that an instance belongs to a particular class (e.g., what is the probability that this email is spam?). If the estimated probability is greater than a given threshold (typically 50%), then the model predicts that the instance belongs to that class (called the positive class, labeled “1”), and otherwise it predicts that it does not (i.e., it belongs to the negative class, labeled “0”). This makes it a binary classifier. 15 | 16 | So how does logistic regression work? Just like a linear regression model, a logistic regression model computes a weighted sum of the input features (plus a bias term), but instead of outputting the result directly like the linear regression model does, it outputs the logistic of this result. 17 | 18 | $$ f_{\mathbf{w},b}(\mathbf{x}^{(i)}) = g(\mathbf{w} \cdot \mathbf{x}^{(i)} + b ) \tag{1} $$ 19 | 20 | where 21 | 22 | $$g(z) = \frac{1}{1+e^{-z}}\tag{2}$$ 23 | 24 | ![image.png](lr_images/sigmoid.PNG) 25 | 26 | Logistic function 27 | 28 | ## Training and Cost Function 29 | Now you know how a logistic regression model estimates probabilities and makes predictions. But how is it trained? The objective of training is to set the parameter vector θ so that the model estimates high probabilities for positive instances (y = 1) and low probabilities for negative instances (y = 0).This idea is captured by the cost function shown in below equation for a single training instance: 30 | 31 | 32 | $$ 33 | loss(f_{\mathbf{w},b}(\mathbf{x}^{(i)}), y^{(i)}) = \begin{cases} 34 | - \log\left(f_{\mathbf{w},b}\left( \mathbf{x}^{(i)} \right) \right) & \text{if $y^{(i)}=1$}\\ 35 | - \log \left( 1 - f_{\mathbf{w},b}\left( \mathbf{x}^{(i)} \right) \right) & \text{if $y^{(i)}=0$} 36 | \end{cases} 37 | $$ 38 | 39 | * $$f_{\mathbf{w},b}(\mathbf{x}^{(i)})$$ is the model's prediction, while $$y^{(i)}$$ is the target value. 40 | * $$f_{\mathbf{w},b}(\mathbf{x}^{(i)}) = g(\mathbf{w} \cdot\mathbf{x}^{(i)}+b)$$ where function $$g$$ is the sigmoid function. 41 | 42 | 43 | This cost function makes sense because –log(t) grows very large when t approaches 0, so the cost will be large if the model estimates a probability close to 0 for a positive instance, and it will also be large if the model estimates a probability close to 1 for a negative instance. On the other hand, –log(t) is close to 0 when t is close to 1, so the cost will be close to 0 if the estimated probability is close to 0 for a negative instance or close to 1 for a positive instance, which is precisely what we want. 44 | 45 | The cost function over the whole training set is the average cost over all training instances. It can be written in a single expression called the log loss: 46 | 47 | $$ J(\mathbf{w},b) = \frac{1}{m} \sum_{i=0}^{m-1} \left[ loss(f_{\mathbf{w},b}(\mathbf{x}^{(i)}), y^{(i)}) \right] \tag{4}$$ 48 | 49 | where 50 | 51 | $$loss(f_{\mathbf{w},b}(\mathbf{x}^{(i)}), y^{(i)}) = -y^{(i)} \log\left(f_{\mathbf{w},b}\left( \mathbf{x}^{(i)} \right) \right) - \left( 1 - y^{(i)}\right) \log \left( 1 - f_{\mathbf{w},b}\left( \mathbf{x}^{(i)} \right) \right) \tag{5}$$ 52 | 53 | ## Logistic Regression using Scikit-Learn 54 | 55 | We will use penguins dataset. We will try to predict the penguin species using the culmen information. This dataset contains three types of **Adelie**, **Gentoo** and **Chinstrap** penguins. 56 | 57 | 58 | ```python 59 | import pandas as pd 60 | 61 | penguins = pd.read_csv("penguins.csv") 62 | penguins = penguins.dropna(axis=0) 63 | 64 | bill_columns = ["bill_length_mm", "bill_depth_mm"] 65 | penguins.species.unique() 66 | ``` 67 | 68 | 69 | 70 | 71 | array(['Adelie', 'Gentoo', 'Chinstrap'], dtype=object) 72 | 73 | 74 | 75 | For model fitting, we will separate the target from the data and we will create a training and a testing set. 76 | 77 | 78 | ```python 79 | from sklearn.model_selection import train_test_split 80 | 81 | x = penguins[bill_columns] 82 | y = penguins.species 83 | 84 | x_train,x_test,y_train , y_test = train_test_split(x,y,test_size=.80 ,random_state=42) 85 | 86 | ``` 87 | 88 | Scikit-learn provides the class ``LogisticRegression`` which implements this algorithm. 89 | 90 | 91 | ```python 92 | from sklearn.pipeline import make_pipeline 93 | from sklearn.preprocessing import StandardScaler 94 | from sklearn.linear_model import LogisticRegression 95 | 96 | logistic_regression = make_pipeline( 97 | StandardScaler(), LogisticRegression(penalty="none") 98 | ) 99 | logistic_regression.fit(x_train, y_train) 100 | accuracy = logistic_regression.score(x_test, y_test) 101 | print(f"Accuracy on test set: {accuracy:.3f}") 102 | ``` 103 | 104 | Accuracy on test set: 0.966 105 | 106 | 107 | Since we are dealing with a classification problem containing only 2 features, it is then possible to observe the decision function boundary. The boundary is the rule used by our predictive model to affect a class label given the feature values of the sample. 108 | 109 | 110 | ```python 111 | import seaborn as sns 112 | import matplotlib.pyplot as plt 113 | from sklearn.inspection import DecisionBoundaryDisplay 114 | 115 | 116 | DecisionBoundaryDisplay.from_estimator( 117 | logistic_regression, x_test, response_method="predict", cmap="RdBu_r", alpha=0.5 118 | ) 119 | sns.scatterplot( 120 | data=x_test, x=bill_columns[0], y=bill_columns[1], 121 | hue=y, palette=["tab:red", "tab:blue","tab:green"]) 122 | _ = plt.title("Decision boundary of the trained\n Logistic Regression") 123 | 124 | ``` 125 | 126 | 127 | 128 | ![png](Logistic_Regression_files/Logistic_Regression_18_0.png) 129 | 130 | 131 | 132 | ## References 133 | - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition 134 | - Machine learning in Python with scikit-learn 135 | - Machine Learning Specialization, Coursera 136 | 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reducing the number of variables or features while preserving their essential information. By extracting the most meaningful and representative features, dimensionality reduction methods enhance computational efficiency, mitigate the curse of dimensionality, and improve model performance. Through the process of dimensionality reduction, high-dimensional data can be visualized, interpreted, and analyzed more effectively, enabling better insights and decision-making. 14 | 15 | ## The Curse of Dimensionality 16 | 17 | The Curse of Dimensionality refers to the challenges and limitations that arise when working with high-dimensional data. As the number of variables or features increases, the available data becomes increasingly sparse, making it difficult to find meaningful patterns or relationships. In high-dimensional spaces, distances between data points become less informative, and the amount of data required to obtain reliable statistical estimates grows exponentially. This phenomenon leads to increased computational complexity, overfitting, and reduced generalization performance of machine learning models. The Curse of Dimensionality underscores the importance of dimensionality reduction techniques to mitigate these issues and extract meaningful information from high-dimensional data. 18 | 19 | ## Main Approaches for Dimensionality Reduction 20 | 21 | ### Projection 22 | 23 | In real-world problems, training data is often not evenly distributed across all dimensions. Some features may be nearly constant, while others may be highly correlated. This creates a lower-dimensional subspace within the higher-dimensional space where all training instances are concentrated. To illustrate, imagine a 3D dataset where all instances lie close to a 2D plane, Figure 1. By projecting each instance onto this plane, we can effectively reduce the dataset's dimensionality from 3D to 2D, represented by new features z1 and z2, Figure 2. 24 | 25 | ![image.png](PCA_images/fig0.PNG) 26 | 27 | Figure 1. A 3D dataset lying close to a 2D subspace 28 | 29 | ![image.png](PCA_images/fig2.PNG) 30 | 31 | Figure 2. The new 2D dataset after projection 32 | 33 | ### Manifold Learning 34 | 35 | Manifold learning aims to uncover the underlying structure or manifold in high-dimensional data. It assumes that the data lies on or near a lower-dimensional manifold embedded within the higher-dimensional space. By mapping the data from the high-dimensional space to a lower-dimensional space, manifold learning algorithms aim to preserve the essential geometric relationships and structures of the data. This allows for better visualization, interpretation, and analysis of complex datasets. Manifold learning methods, such as t-SNE and Isomap, have proven effective in revealing hidden patterns and clusters in data that are not easily discernible in the original high-dimensional space. 36 | 37 | ## Principal component analysis (PCA) 38 | 39 | PCA is widely regarded as the most popular method for dimensionality reduction. It begins by identifying the hyperplane that is positioned closest to the data and subsequently projects the data onto this hyperplane. 40 | 41 | ### Preserving the Variance 42 | 43 | In order to project the training set onto a lower-dimensional hyperplane, it is crucial to choose the appropriate hyperplane. By considering a 2D dataset and different axes (1D hyperplanes), the projection results demonstrate varying levels of preserved variance, Figure 3. The solid line projection retains the maximum variance, the dotted line projection preserves minimal variance, and the dashed line projection maintains an intermediate amount of variance. Selecting the axis that preserves the highest variance is reasonable, as it minimizes information loss compared to other projections. This choice can also be justified by minimizing the mean squared distance between the original dataset and its projection. This simple concept forms the basis of Principal Component Analysis (PCA). 44 | 45 | ![image.png](PCA_images/fig3.PNG) 46 | 47 | Figure 3. Selecting the subspace on which to project 48 | 49 | ### Principal Components 50 | 51 | PCA identifies the primary axis that captures the most variance in the training set. It then discovers a second orthogonal axis that explains the largest remaining variance. This process continues for higher-dimensional datasets, where PCA identifies additional axes, known as principal components (PCs). In Figure 3, the solid line represents the first PC, while the dotted line represents the second PC. Each PC corresponds to an axis along which specific vectors (c1, c2) lie. 52 | 53 | ### How can you find the principal components of a training set? 54 | 55 | There is a widely used matrix factorization method called singular value decomposition (SVD) exists, which can break down the training set matrix $$X$$ into the product of three matrices: $$U \Sigma V^T$$. Among these matrices, $$V$$ contains the unit vectors that represent the principal components sought in the analysis. 56 | 57 | ### Projecting Down to d Dimensions 58 | 59 | 60 | After identifying the principal components, dimensionality reduction can be achieved by projecting the dataset onto the hyperplane defined by the selected principal components. This projection aims to preserve maximum variance. To obtain a reduced dataset $$X_{d\text{-proj}}$$ of dimensionality $$d$$, the training set matrix $$X$$ is multiplied by the matrix $$W_d$$, which contains the first d columns of $$V$$. This operation is represented as $$X_{d\text{-proj}} = XW_d$$ 61 | 62 | ### PCA Implementation from scratch using numpy 63 | 64 | 65 | ```python 66 | import numpy as np 67 | 68 | def PCA(X, n_components): 69 | # Center the data 70 | X_centered = X - np.mean(X, axis=0) 71 | 72 | # Perform SVD 73 | U, Sigma, Vt = np.linalg.svd(X_centered) 74 | 75 | # Select the top k eigenvectors (principal components) 76 | principal_components = Vt[:n_components].T 77 | 78 | # Project the data onto the principal components 79 | X_pca = np.dot(X_centered, principal_components) 80 | 81 | # Return the transformed data and the principal components 82 | return X_pca, principal_components 83 | ``` 84 | 85 | ### Using Scikit-Learn 86 | 87 | 88 | ```python 89 | from sklearn.decomposition import PCA 90 | from sklearn.datasets import make_blobs 91 | 92 | # Create a random dataset 93 | X, y = make_blobs(n_samples=1000, n_features=10, centers=3, random_state=42) 94 | 95 | # Instantiate PCA with desired number of components 96 | n_components = 2 97 | pca = PCA(n_components=n_components) 98 | 99 | # Fit and transform the data 100 | X_pca = pca.fit_transform(X) 101 | 102 | # Access the explained variance ratio 103 | explained_variance_ratio = pca.explained_variance_ratio_ 104 | 105 | # Print the results 106 | print(f"Original shape: {X.shape}") 107 | print(f"Reduced shape: {X_pca.shape}") 108 | print(f"Explained variance ratio: {explained_variance_ratio}") 109 | ``` 110 | 111 | Original shape: (1000, 10) 112 | Reduced shape: (1000, 2) 113 | Explained variance ratio: [0.74942445 0.2138575 ] 114 | 115 | 116 | An additional valuable information is the explained variance ratio associated with each principal component, which can be accessed through the variable `explained_variance_ratio_`. This ratio represents the proportion of the dataset's variance captured by each principal component. 117 | 118 | Rather than randomly selecting the desired number of dimensions for dimensionality reduction, a simpler approach is to choose the number of dimensions that collectively explain a significant portion of the variance, such as 95%. 119 | 120 | 121 | ```python 122 | pca = PCA(n_components=0.95) 123 | X_reduced = pca.fit_transform(X) 124 | print(f"Number of components: {pca.n_components_}") 125 | ``` 126 | 127 | Number of components: 2 128 | 129 | 130 | ## Variants of PCA 131 | 132 | ### Randomized PCA 133 | 134 | 135 | When the `svd_solver` hyperparameter is set to "randomized" in Scikit-Learn, it employs a stochastic algorithm known as randomized PCA. This algorithm efficiently approximates the first d principal components. Its computational complexity is $$O(m × d^2) + O(d^3)$$, which is significantly faster than the full SVD approach with a complexity of $$O(m × n^2) + O(n^3)$$, particularly when d is much smaller than n. 136 | 137 | 138 | ```python 139 | rnd_pca = PCA(n_components=5, svd_solver="randomized", random_state=42) 140 | X_reduced = rnd_pca.fit_transform(X) 141 | ``` 142 | 143 | ### Incremental PCA 144 | 145 | Incremental PCA (IPCA) is a variant of Principal Component Analysis (PCA) that is designed to handle large datasets that may not fit entirely into memory. It overcomes the memory limitations of standard PCA by processing the data in mini-batches or chunks rather than requiring the entire dataset to be present at once. 146 | 147 | The IPCA algorithm performs PCA in an incremental manner by sequentially processing subsets of the dataset. It updates the principal components incrementally as new data batches are introduced, allowing for efficient memory usage. This makes it suitable for online or streaming scenarios where new data arrives continuously. 148 | 149 | Scikit-learn provides an implementation of Incremental PCA `sklearn.decomposition.IncrementalPCA` that you can use to apply IPCA to your datasets. 150 | 151 | ## Other Dimensionality Reduction Techniques 152 | 153 | `sklearn.manifold.MDS` 154 | 155 | Multidimensional scaling (MDS) reduces dimensionality while trying to preserve the distances between the instances. Random projection does that for highdimensional data, but it doesn’t work well on low-dimensional data. 156 | 157 | `sklearn.manifold.Isomap` 158 | 159 | Isomap constructs a graph by establishing connections between neighboring instances, and then performs dimensionality reduction while aiming to maintain the geodesic distances between the instances. Geodesic distance refers to the shortest path, in terms of the number of nodes, between two connected nodes in the graph. 160 | 161 | `sklearn.manifold.TSNE` 162 | 163 | 164 | t-SNE, which stands for t-distributed stochastic neighbor embedding, is a dimensionality reduction technique that aims to maintain the proximity of similar instances and separate dissimilar instances. It is commonly employed for data visualization purposes, specifically to visualize clusters of instances within high-dimensional spaces. 165 | 166 | `sklearn.discriminant_analysis.LinearDiscriminantAnalysis` 167 | 168 | Linear discriminant analysis (LDA) is a linear classification algorithm that identifies the most distinguishing directions between classes during training. These directions are used to define a hyperplane onto which the data can be projected. The advantage of LDA is that the projection maximizes the separation between classes, making it a valuable dimensionality reduction technique prior to applying another classification algorithm (unless LDA alone is suitable). 169 | 170 | ## References 171 | - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition 172 | 173 | -------------------------------------------------------------------------------- /pages/lectures/Unsupervised/PCA_images/fig0.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/pages/lectures/Unsupervised/PCA_images/fig0.PNG -------------------------------------------------------------------------------- /pages/lectures/Unsupervised/PCA_images/fig2.PNG: -------------------------------------------------------------------------------- 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TOC 21 | {:toc} 22 | 23 | --- 24 | 25 | ## Books 26 | - [An Introduction to Statistical Learning, Second Edition](https://www.statlearning.com/), 2021, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. 27 | - [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/), 2009, 2nd edition, by Trevor Hastie Robert Tibshirani, Jerome Friedman. 28 | - [Probabilistic Machine Learning - An Introduction](https://probml.github.io/pml-book/book1.html), 2021, by Kevin Patrick Murphy. 29 | - [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/), 2022, 3rd edition, by Aurélien Géron. 30 | - [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf), 2006, by Christopher Bishop. 31 | - [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf), 2004, by Lieven Vandenberghe, Stephen Boyd. 32 | - [Reinforcement Learning: An Introduction](http://www.incompleteideas.net/book/the-book-2nd.html), 2018, Second Edition, by Richard S. Sutton and Andrew G. Barto. 33 | - [Python Data Science Handbook](https://www.oreilly.com/library/view/python-data-science/9781491912126/), 2016, by by Jake VanderPlas. 34 | - [Mathematic for Machine Learning](https://mml-book.github.io/book/mml-book.pdf) 35 | - [Matrix Methods in Data Mining and Pattern Recognition](https://epubs.siam.org/doi/book/10.1137/1.9780898718867) 36 | - [Think Stats](https://www.oreilly.com/library/view/think-stats-2nd/9781491907344/), 2014, by Allen B. Downey 37 | - [Practical Statistics for Data Scientists](https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/), 2017, by Peter Bruce, Andrew Bruce 38 | - [The Kaggle Book](https://www.oreilly.com/library/view/the-kaggle-book/9781801817479/), 2022, by Konrad Banachewicz, Luca Massaron, Anthony Goldbloom 39 | - [Data Visualisation: A Handbook for Data Driven Design](https://www.amazon.com/Data-Visualisation-Handbook-Driven-Design/dp/1473912148), 2016, by Andry Krik 40 | - [Truthful Art, The: Data, Charts, and Maps for Communication](https://www.amazon.com/Truthful-Art-Data-Charts-Communication/dp/0321934075), 2016, by Alberto Cario 41 | - [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf) 42 | - [Machine Learning For Dummies](https://www.ibm.com/downloads/cas/GB8ZMQZ3) 43 | - [Master Machine Learning Algorithms](https://machinelearningmastery.com/master-machine-learning-algorithms/) 44 | - [Python for Data Analysis](https://www.oreilly.com/library/view/python-for-data/9781449323592/), 2012, by Wes McKinney 45 | 46 | --- 47 | 48 | ## Courses 49 | - [CS229 - Machine Learning](https://see.stanford.edu/Course/CS229), Introduction to machine learning and statistical pattern recognition from Stanford. 50 | - [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction) This is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online which covers fundamentals of machine learning 51 | - [Machine Learning Crash Course — Google AI](https://learndigital.withgoogle.com/digitalgarage/course/machine-learning-crash-course) This course explains the fundamentals of machine learning through a package of courses that include interactive visualizations of algorithms in operation, real-world case studies, literature developed specifically for ML beginners, video lectures by Google researchers, and real-world examples. 52 | - [Data Science Foundations: Python Scientific Stack ](https://www.linkedin.com/learning/data-science-foundations-python-scientific-stack-17064277?replacementOf=data-science-foundations-python-scientific-stack-2022), covers the major packages used throughout the data science process: numpy, pandas, matplotlib, scikit-learn, and others. 53 | - [Applied Data Science with Python Specialization](https://www.coursera.org/specializations/data-science-python) 54 | - [Linear Algebra - MIT](https://youtube.com/playlist?list=PL49CF3715CB9EF31D), Gilbert Strang 55 | - [Pandas Bootcamp - Udemy](https://www.udemy.com/course/the-pandas-bootcamp/) 56 | 57 | --- 58 | 59 | ## Videos 60 | - [3Blue1Brown](https://www.youtube.com/c/3blue1brown/) - some combination of math and entertainment. Difficult problems made simple with great animations. 61 | - [StatQuest](https://www.youtube.com/c/joshstarmer/) - breaks down the major methodologies into easy to understand pieces. 62 | - [Krish Naik](https://www.youtube.com/@krishnaik06) 63 | 64 | --- 65 | 66 | ## Web pages 67 | - [Kaggle mini courses](https://www.kaggle.com/learn) - Gain the skills you need to do independent data science projects. 68 | - [Data Science course SBU, Fall 2021](https://sk7w4tch3r.github.io/CS-SBU-DataScience/) - This course is an introduction to data science. It is an overview of the latest primary knowledge and skills in the data science criteria, focusing on the basics of linear algebra, probability and statistics, first steps of working with data, data wrangling, exploratory data analysis, visualization techniques, and the essentials of supervised learning, time series analysis, and unsupervised methods. There are no particular prerequisites, and we tried to cover everything to our potential. 69 | - [DataToViz](https://www.data-to-viz.com/) 70 | - [CS 229 - Machine Learning](https://stanford.edu/~shervine/l/fa/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks) 71 | 72 | -------------------------------------------------------------------------------- /pages/resources/resources.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: Resources 4 | nav_order: 10 5 | has_children: true 6 | permalink: /resources 7 | --- 8 | 9 | # Resources 10 | 11 | Papers, Links, + Other References to Knowledge & Information 12 | {: .fs-6 .fw-300 } 13 | -------------------------------------------------------------------------------- /pages/resources/slides.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | title: Slides 4 | nav_order: 2 5 | parent: Resources 6 | has_children: false 7 | permalink: /resources/slides 8 | --- 9 | 10 | # Slides 11 | 12 | - [Persian handouts](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/Persian-handouts.rar) 13 | - [Introduction](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/1-introduction-handout.pdf) 14 | - [Linear Regression](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/2-linear_regression-handout.pdf) 15 | - [Logistic Regression](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/3-classification-handout.pdf) 16 | - [Cross Validation](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/4-cv_boot-handout.pdf) 17 | - [Linear Model Selection and Regularization](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/5-%20model_selection-handout.pdf) 18 | - [Support Vector Machines](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/svm-handout.pdf) 19 | - [Desicion Trees](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/trees-handout.pdf) 20 | - [Unsupervised Learning](https://github.com/scheshmi/CS-SBU-MachineLearning-2023/blob/main/slides/unsupervised-handout.pdf) 21 | -------------------------------------------------------------------------------- /pages/schedule.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Schedule 4 | description: The weekly event schedule. 5 | nav_order: 5 6 | --- 7 | 8 | # Weekly Schedule 9 | 10 | {% for schedule in site.schedules %} 11 | {{ schedule }} 12 | {% endfor %} 13 | -------------------------------------------------------------------------------- /pages/staff.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Team 4 | nav_order: 8 5 | permalink: /staff 6 | description: Meet our awesome team members! 7 | --- 8 | 9 | # Team 10 | 11 | Meet our awesome team members! 12 | {: .fs-6 .fw-300 } 13 | 14 | 15 | ## Instructor 16 | 17 | {% assign president = site.staffers | where: 'role', 'President' %} 18 | {% for staffer in president %} 19 | {{ staffer }} 20 | {% endfor %} 21 | 22 | {% assign officers = site.staffers | where: 'role', 'Officer' %} 23 | {% assign num_officers = officers | size %} 24 | {% if num_officers != 0 %} 25 | 26 | ## Assistants 27 | 28 | {% for staffer in officers %} 29 | {{ staffer }} 30 | {% endfor %} 31 | 32 | {% assign advisors = site.staffers | where: 'role', 'Advisor' %} 33 | 34 | 35 | {% for staffer in advisors %} 36 | {{ staffer }} 37 | {% endfor %} 38 | {% endif %} 39 | 40 | {% assign member = site.staffers | where: 'role', 'Member' %} 41 | {% assign num_member = member | size %} 42 | {% if num_member != 0 %} 43 | 44 | ## Members 45 | 46 | {% for staffer in member %} 47 | {{ staffer }} 48 | {% endfor %} 49 | {% endif %} 50 | -------------------------------------------------------------------------------- /slides/1-introduction-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/1-introduction-handout.pdf -------------------------------------------------------------------------------- /slides/2-linear_regression-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/2-linear_regression-handout.pdf -------------------------------------------------------------------------------- /slides/3-classification-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/3-classification-handout.pdf -------------------------------------------------------------------------------- /slides/4-cv_boot-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/4-cv_boot-handout.pdf -------------------------------------------------------------------------------- /slides/5- model_selection-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/5- model_selection-handout.pdf -------------------------------------------------------------------------------- /slides/Persian-handouts.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/Persian-handouts.rar -------------------------------------------------------------------------------- /slides/nonlinear-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/nonlinear-handout.pdf -------------------------------------------------------------------------------- /slides/statistical_learning-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/statistical_learning-handout.pdf -------------------------------------------------------------------------------- /slides/svm-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/svm-handout.pdf -------------------------------------------------------------------------------- /slides/trees-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/trees-handout.pdf -------------------------------------------------------------------------------- /slides/unsupervised-handout.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/scheshmi/CS-SBU-MachineLearning-2023/9589857eb7b01decd5fd1ccd0cc0eabbdd4ede2e/slides/unsupervised-handout.pdf --------------------------------------------------------------------------------