Lecture Notes
Topics will be selected from learning models, learning theory, and large-scale optimization.
- Lecture 1. Introduction (pdf)
- Lecture 2. Supervised Learning (pdf)
- Linear Regression, Weighted Linear Regression, Perceptron, Exponential Family, Generalized Linear Models
- Suppl : Linear algebra review (pdf)
- Lecture 3. Generative Models (pdf)
- Gaussian discriminative analysis, naive Bayes, Laplace smoothing
- Suppl : Probability theory review (pdf)
- HW1 is out!
- Lecture 4. Support vector machines (pdf)
- Suppl: Convex optimization (pdf1, pdf2)
- Suppl: SMO paper by John Platt 1998 (link), String kernel by Leslie & Kuang 2004 (link)
- Lecture 5. HW1 Discussion & Learning Theory part I (pdf)
- Suppl: Hoeffding's inequality (pdf)
- HW2 is out!
- Lecture 6. Learning Theory part 2
- Lecture 7. HW2 Discussion, SMO algorithm, Regularization and Model Selection (pdf)
- Midterm (April 23, in class)
- Lecture 8. EM algorithm (pdf1, pdf2)
- Bayesian inference (remainder of L7)
- Midterm review
- Lecture 9. Mixture of Gaussian, Factor Analysis (pdf)
- Lecture 10. PCA (pdf) and ICA (pdf)
- Lecture 11. Reinforcement learning and control (pdf)
- Lecture 12. LQR, DDP, and LQG (pdf)
- Lecture 13. POMDP, Policy Search, Reinforce, Pegasus
- Final Exam (June 11, in class)