Lectures :

1. Lecture 1: Setting Up the Learning Problem [Slides] [lecnotes]
   

2. Lecture 2: Statistical Learning Framework [lecnotes]
   

3. Lecture 3: Statistical Learning, Empirical Risk Minimization and Uniform Convergence [lecnotes]
   

4. Lecture 4: Empirical Risk Minimization, Uniform Convergence and Rademacher Complexity [lecnotes]
   

5. Lecture 5: Binary Classification, Rademacher Complexity, Growth Function and VC Dimension [lecnotes]
   

6. Lecture 6: Rademacher Complexity Properties and Examples [lecnotes]
   

7. Lecture 7: Rademacher Complexity Properties and Examples [lecnotes]
   

8. Lecture 8: Algorithmic Stability [lecnotes]
   

9. Lecture 9: Algorithmic Stability [lecnotes]
   

10. Lecture 10: Algorithmic Stability [lecnotes]
    

11. Lecture 11: Online Learning, Exponential Weights Algorithm [lecnotes]
    

12. Lecture 12: Online Linear and Online Convex Optimization [lecnotes]
    

13. Lecture 13: Boosting [lecnotes]
    

14. Lecture 14: Stochastic Multiarmed Bandit [lecnotes]
    

15. Lecture 15: Stochastic Multiarmed Bandit [lecnotes]
    

16. Lecture 16: Stochastic Multiarmed Bandit [lecnotes]
    

17. Lecture 17: Computational Learning Theory [lecnotes]
    

18. Lecture 18: Computational Learning Theory [lecnotes]
    

19. Lecture 19: Computational Learning Theory: Representation Free [lecnotes]
    

20. Lecture 20: Computational Learning Theory: Representation Free [lecnotes]
    

21. Lecture 21: Differential Privacy [lecnotes] [slides]
    

22. Lecture 22: Differential Privacy [lecnotes]
    

23. Lecture 23: Fairness in Machine Learning [slides]
    

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