├── LICENSE ├── README.md ├── datasets ├── 4bit.csv ├── ENB2012_data.csv ├── abalone data │ ├── abalone.data │ └── abalone.names ├── iris.csv ├── iris.data ├── pima-indians-diabetes.csv ├── test.csv ├── train.csv ├── wine.csv ├── winequality-red.csv ├── winequality-white.csv └── xor.csv ├── lectures ├── MATH5836_Lec_0_Calculus.pdf ├── MATH5836_Lec_0_LinearAlgebra.pdf ├── MATH5836_Lec_0_Probability.pdf ├── MATH5836_Lec_10_Emerging_Topics_in_Machine_Learning.pdf ├── MATH5836_Lec_1_Data_and_Linear_Regression.pdf ├── MATH5836_Lec_2_Classification_and_Regularization.pdf ├── MATH5836_Lec_3_Introduction_to_Neural_Networks.pdf ├── MATH5836_Lec_4_Advanced_Techniques_for_Neural_Networks.pdf ├── MATH5836_Lec_5_Bayesian_Neural_Networks.pdf ├── MATH5836_Lec_7_Decision_Trees_Random_Forest.pdf ├── MATH5836_Lec_8_Ensemble_Learning.pdf └── MATH5836_Lec_9_Unsupervised_Learning.pdf ├── notebooks ├── week00 │ ├── week0-Python-basics.ipynb │ ├── week0-functions-python.ipynb │ └── week0-loading-saving-files.ipynb ├── week01 │ ├── week1-exercise1-regression-error-measurements.ipynb │ ├── week1-exercise2-classification-error-measurements.ipynb │ └── week1-exercise3-linear-regression-on-sythetic-data.ipynb ├── week02 │ ├── week2-exercise1-logistic-regression-on-sythetic-data.ipynb │ ├── week2-exercise2-softmax-regression-on-sythetic-data.ipynb │ └── week2-exercise3-softmax-regression-on-real-data.ipynb ├── week03 │ ├── week3-exercise1-neural-network-classifier.ipynb │ ├── week3-exercise2-neural-network-regressor.ipynb │ └── week3-exercise3-keras-based-neural-networks.ipynb ├── week04 │ ├── week4-exercise1-compare-optimizers.ipynb │ ├── week4-exercise2-application-of-dropout.ipynb │ └── week4-exercise3-GridSearchCV.ipynb ├── week05 │ ├── week5-exercise1-simple-bayesian-inference.ipynb │ ├── week5-exercise2-bayesian-linear-regression.ipynb │ └── week5-exercise3-bayesian-neural-network.ipynb ├── week07 │ ├── week7-exercise1-decision-tree-Iris-dataset.ipynb │ ├── week7-exercise2-decision-tree-sklearn.ipynb │ └── week7-exercise3-random-forest-bagging.ipynb ├── week08 │ ├── week8-exercise1-boosting-pima-indian-diabetes.ipynb │ ├── week8-exercise2-voting-ensembles-on-energy-data.ipynb │ └── week8-exercise3-stacking-on-abalone-data.ipynb ├── week09 │ ├── week9-exercise1-pca-vs-manifold-learning.ipynb │ ├── week9-exercise2-Kmeans-clustering-wine-data.ipynb │ └── week9-exercise3-DBSCAN-wine-data.ipynb └── week10 │ ├── week10_exercise1_magnitude_pruning_demo.ipynb │ ├── week10_exercise2_knowledge_distillation_demo.ipynb │ └── week10_exercise3_masked_representation_learning.ipynb └── tutorials ├── week01 ├── tutorial-1-Py-solution-coding-task.ipynb ├── tutorial-1-R-solution-coding-task.R ├── tutorial-1-solution-theoretical-task.pdf └── tutorial-1-theoretical-task.pdf ├── week02 ├── tutorial-2-logistic-regression-breast-cancer-dataset.ipynb └── tutorial-2-solution-logistic-regression-breast-cancer-dataset.ipynb ├── week03 ├── 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