Problem sets, additional material and prerequisites
- Statistical learning: Problem set 1, Problem set 2, Problem set 3.
- Convex optimization: Problem Set 1.
- Bandits: Problem Set 1.
- Reinforcement learning: Problem set 1, Problem set 2, Problem set 3
- Deep learning: Problem set 1, Problem set 2, Problem set 3.
- Statistical learning lectures:
- Slides from Lecture IV: McDiarmid's Inequality, Rademacher proof
- Slides from Lecture V: Boosting toy example, boosting learning curves, analysis of training error, margins movie.
- Rob Schapire course on Theoretical Machine Learning.
- Linear Algebra (sample course)
- Probability (sample course)
- Multivariable calculus (sample course).
- Real Analysis (sample course).
- Foundations of Machine Learning, Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
- Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David
- Convex Optimization: Algorithms and Complexity, Sébastien Bubeck
- Theory of Classification: A Survey of Some Recent Advances, Stéphane Boucheron, Olivier Bousquet and Gábor Lugosi
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Sébastien Bubeck and Nicolò Cesa-Bianchi
- Algorithms for Reinforcement Learning, Csaba Szepesvári
- Appendix E (Neural Networks), Principles of Neural Science, Sebastian Seung and Rafael Yuste
Papers for Friday, August 9 presentations
Problem Sets:
Additional Material
Suggested Prerequisites:
Online resources about the math of machine learning