Week | Content |
Notation | |
Week-1 | PCA |
Week-2 | Kernel PCA |
Week-3 | K-Means |
Week-4 | Estimation, MLE, Bayesian, EM algorithm |
Week-5 | Linear regression, Kernel regression |
Week-6 | Regularization, Ridge and Lasso regression |
Week-7 | Classification, KNN, Decision Trees |
Week-8 | Discriminative and generative models, Naive Bayes |
Week-9 | Perceptron, Logistic Regression |
Week-10 | Hard-Margin SVM |
Week-11 | Soft-Margin SVM, Bagging, Boosting, AdaBoost |
Week-12 | Loss functions for classification, Neural networks |