Course Materials
Handouts |
- Handout #1: Course Information (HTML)
- Handout #2: Tentative Course Schedule (HTML)
- Handout #3: Problem Set 1 (ps) (pdf) (data)
- Handout #4: Project Guidelines (ps) (pdf)
- Handout #5: Problem Set 2 (ps) (pdf) (data)
- Handout #6: Simplified SMO Algorithm (ps) (pdf)
- Handout #7: Problem set 1 solutions (pdf)
- Handout #8: Problem Set 3 (ps) (pdf) (data)
- Handout #9: Mid-quarter Course Survey (pdf)
- Handout #10: Practice Midterm Exam (ps)
(pdf) - Handout #11: Practice Midterm Solutions (pdf)
- Handout #12: Problem Set 2 Solutions (pdf)
- Handout #13: Midterm Solutions (pdf)
- Handout #14: Problem Set 4 (ps) (pdf) (data)
- Handout #15: Problem Set 3 Solutions (pdf)
- Handout #16: Related AI classes (pdf)
- Handout #17: Problem Set 4 Solutions (pdf)
Lecture Notes |
- Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms
- Lecture notes 2 (ps) (pdf) Generative Algorithms
- Lecture notes 3 (ps) (pdf) Support Vector Machines
- Lecture notes 4 (ps) (pdf) Learning Theory
- Lecture notes 5 (ps) (pdf) Regularization and Model Selection
- Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm. (optional reading)
- Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering.
- Lecture notes 7b (ps) (pdf) Mixture of Gaussians
- Lecture notes 8 (ps) (pdf) The EM Algorithm
- Lecture notes 9 (ps) (pdf) Factor Analysis
- Lecture notes 10 (ps) (pdf) Principal Components Analysis
- Lecture notes 11 (ps) (pdf) Independent Components Analysis
- Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control