Lectures :

1. Lecture 1: Setting Up the Learning Problem [Slides] [lecnotes]
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2. Lecture 2: Statistical Learning, Empirical Risk Minimization and Uniform Convergence [Slides] [lecnotes]
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3. Lecture 3: ERM, Uniform Convergence and Rademacher Complexity [Slides] [lecnotes]
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4. Lecture 4: Rademacher Complexity, Growth Function and VC Dimension [Slides] [lecnotes]
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5. Lecture 5: Rademacher Complexity, Growth Function and VC Dimension [Slides] [lecnotes]
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6. Lecture 6: Properties of Rademacher Complexity and Examples [lecnotes]
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7. Lecture 7: Properties of Rademacher Complexity and Examples [lecnotes]
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8. Lecture 8: Algorithmic Stability and Learning [lecnotes]
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9. Lecture 9: Algorithmic Stability and Learning [lecnotes]
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10. Lecture 10: Algorithmic Stability and Learning [lecnotes]
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11. Lecture 11: Online Learning, Exponential Weights Algorithm [lecnotes]
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12. Lecture 12: Online Linear and Convex Optimization [lecnotes]
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13. Lecture 13: Stochastic Convex Optimization and SGD [lecnotes]
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14. Lecture 14: Boosting and Online Learning [lecnotes]
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15. Lecture 15: Stochastic Multi-armed Bandit [lecnotes]
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16. Lecture 16: Stochastic Multi-armed Bandit and Non-Stochastic Multi-armed Bandit [lecnotes]
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17. Lecture 17: Computational Complexity of Learning [lecnotes]
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18. Lecture 18: Computational Complexity of Learning: Representation Free [lecnotes]
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19. Lecture 19: Computational Complexity of Learning: Representation Free [lecnotes]
    

20. Lecture 20: Differential Privacy and Learning [slides] [lecnotes]
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21. Lecture 21: Reusing Holdout Set Via Differential Privacy [slides]
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22. Lecture 22: Reusing Holdout Set Continued [slides]
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23. Lecture 23: Last Lecture [slides]
    

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