| 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 |