Subject to change.
| Date | Topics | Readings |
|---|---|---|
| Tu Aug 28 | Welcome to Advanced Machine Learning! | math4ml , linear algebra (advanced), convex analysis, optimization, probability review |
| Th Aug 30 | Terminologies | review continue |
| Tu Sep 4 | PAC learning definition and probability tools | Chapter 1 of Foundations of Machine Learning Book |
| Th Sep 6 | PAC learning definition and probability tools | A gentle introduction to Concentration Inequalities, Appendix D of Book |
| Tu Sep 11 | Learning with finite hypothesis sets | Textbook Chapter 2.2-2.4 |
| Th Sep 13 | Learning with infinite hypothesis sets | Textbook Chapter 3.1-3.4 |
| Tu Sep 18 | Boosting | Textbook Chapter 6 |
| Th Sep 20 | Boosting | Textbook Chapter 6 |
| Tu Sep 25 | Intro to Latent variable models | Latent Variable Model:Page 2773-2780, Tensor Review with highlights |
| Th Sep 27 | Topic Model | Spectral algorithm for Latent Dirichlet Allocation |
| Tu Oct 2 | Jennrich's algorithm | Lecture Notes |
| Th Oct 4 | Power Method | Lecture Notes |
| Tu Oct 9 | Motivation: why rethink generalization | Understanding deep learning requires rethinking generalization |
| Th Oct 11 | PAC bound for deep nets | A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks |
| Tu Oct 16 | Generalization via compression | Stronger generalization bounds for deep nets via a compression approach |
| Th Oct 18 | Generalization in Deep Learning | Generalization in Deep Learning |
| Tu Oct 23 | Learning Automata: finite automata and exact learning | Textbook Chapter 13.1-13.3 |
| Th Oct 25 | Learning Automata: finite automata and exact learning | Textbook Chapter 13.1-13.3 |
| Tu Oct 30 | Intro to RL: policy | Textbook 14.1-14.3 |
| Th Nov 1 | Planning algorithms | Textbook 14.4 |
| Tu Nov 6 | Learning algortihms | Textbook 14.5 |
| Th Nov 8 | Deep Q Learning | |
| Tu Nov 13 | Off policy evaluation | |
| Th Nov 15 | Contextual Bandits | Lecture Notes |
| Tu Nov 20 | ||
| Thanksgiving break! | ||
| Tu Nov 27 | ||
| Th Nov 29 | Final Presentation | |
| Tu Dec 4 | Final Presentation | |
| Th Dec 6 | Final Presentation |