Lectures :

  1. Lecture 1 : Introduction, course details, what is learning theory, learning frameworks [slides]
    Reference : [1] (ch 1 and 3)

  2. Lecture 2 : Learning frameworks and Minimax Rates [lec]
    Reference : [1] (ch 1 and 3)

  3. Lecture 3: Minimax Rates, Comparing the Learning Frameworks, No Free Lunch Theorem [lec]
    Reference : [1] (ch 1 and 3)

  4. Lecture 4: Statistical Learning, Uniform Convergence [lec]
    Reference : [1] (ch 1 and 3)

  5. Lecture 5: Statistical Learning, Uniform Convergence, MDL, Infinite Classes [lec]

  6. Lecture 6: Symmetrization, Rademacher Complexity, Growth Function and VC dimension [lec]

  7. Lecture 7: Growth Function, VC dimension, Sauer-Shelah-VC Lemma and Massart's finite lemma [lec]

  8. Lecture 8: Massart's finite lemma, Properties of Rademacher Complexity [lec]

  9. Lecture 9: Properties of Rademacher Complexity, Examples [lec]

  10. Lecture 10: Covering Number, Pollard's Bound, Dudley Integral Complexity [lec]

  11. Lecture 11: Wrapping up Statistical Learning Theory [lec]

  12. Lecture 12: Online Learning, Bit Prediction [lec]

  13. Guest Lecture by Bobby Kleinberg: Multiplicative Weights

  14. Lecture 13: Online Learning, Bit Prediction, Cover's result [lec]

  15. Lecture 14: Learning with Covariates, Exponential Weights Algorithm [lec]

  16. Lecture 15: Online Convex Optimization and Online Gradient Descent [lec]

  17. Lecture 16: Online Mirror Descent [lec] [Supplementary material]

  18. Lecture 17: Online Mirror Descent: Strongly convex and Exp-concave losses [lec]

  19. Lecture 18: General Online Learning and Relaxations [lec]

  20. Lecture 19: Sufficient Statistics and Online Learning [lec]

  21. Lecture 20: Burkholder Method [lec]

  22. Lecture 21: Burkholder Method [lec]

  23. Lecture 22: Burkholder Method [lec]

  24. Lecture 23: Matrix Prediction Via Burkholder Method [lec]

  25. Lecture 24: Matrix Prediction Via Burkholder Method [lec]

  26. Lecture 25: Random Playouts and Last Lecture [lec]

  27. Lecture 26: Last Lecture [lec]