This course is a rigorous mathematical introduction to the fundamentals of machine learning.
The class involves weekly homeworks, a midterm exam and a final exam.
| Lec 1 | Introduction and Overview |
| Lec 2 | Linear Regression |
| Lec 3 | Linear Regression – Overfitting. Ridge, Lasso |
| Lec 4 | Classification, Logistic Regression |
| Lec 5 | Generative models for Classification: LDA, Naive Bayes |
| Lec 6 | Convex Functions |
| Lec 7 | Support Vector Machines (SVM) |
| Lec 8 | k-Nearest Neighbors |
| Lec 9 | Decision Trees |
| Lec 10 | Adaboost |
| Lec 11 | Gradient Boosting |
| Lec 12 and 13 | Midterm Exam |
| Lec 14 | Custering |
| Lec 15 | Gaussian Mixture Models |
| Lec 16 | PCA |
| Lec 17 | Low-rank Matrices / Spectral methods |
| Lec 18 | Online Learning |
| Lec 19 | Online Learning (continued) |
| Lec 21 | Ranking |