CS2007: Machine Learning Techniques

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Week	Topic	Lecture Videos	Lecture Slides	Notes PDF	Tutorial Video	Tutorial Slides	Tutorial Colab
1	Introduction; Unsupervised Learning - Representation learning - PCA	🖥️	🎫	📝	🖥️	🖥️	🎫
2	Unsupervised Learning - Representation learning - Kernel PCA	🖥️	🎫	📝	🖥️	🖥️	🎫
3	Unsupervised Learning - Clustering - K-means/Kernel K-means	🖥️	🎫	📝	🖥️	🖥️	🎫
4	Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm	🖥️	🎫	📝	🖥️	🖥️	🎫
5	Supervised Learning - Regression - Least Squares; Bayesian view	🖥️	🎫	📝	🖥️	🖥️	🎫
6	Supervised Learning - Regression - Ridge/LASSO	🖥️	🎫	📝	🖥️	🖥️	🎫
7	Supervised Learning - Classification - K-NN, Decision tree	🖥️	🎫	📝	🖥️	🖥️	🎫
8	Supervised Learning - Classification - Generative Models - Naive Bayes	🖥️	🎫	📝		🖥️	🎫
9	Discriminative Models - Perceptron; Logistic Regression	🖥️	🎫	📝		🖥️	🎫
10	Support Vector Machines	🖥️	🎫	📝
11	Ensemble methods - Bagging and Boosting (Adaboost)	🖥️	🎫
12	Artificial Neural networks; Multiclass classification	🖥️	🎫