Schedule

Subject to change.

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Date Topics Readings Lecture Slides
Tu Sept 1 Introduction Slides
Th Sept 3 Review
  • math4ml
  • Linalg Review
  • Convex Analysis Review
  • Optimization Review
  • Slides
    Tu Sept 8 Concentration Bounds Slides
    Th Sept 10 Concentration Bounds cont. Slides
    Tu Sept 15 PAC Learning
  • Foundations of Machine Learning Chapter 1
  • Foundations of Machine Learning Chapter 2- PAC Learning
  • Slides
    Th Sept 17 PAC Learning Foundations of Machine Learning Chapter 2 Slides
    Tu Sept 22 PAC Learning cont. Slides
    Th Sept 24 PAC Learning cont. Slides
    Tu Sept 29 Boosting Foundations of Machine Learning Chapter 7 Slides
    Th Oct 1 Boosting cont. Foundations of Machine Learning Chapter 7 Slides
    Tu Oct 6 Boosting cont. and Generalization of DNN
  • Understanding Deep Learning Requires Rethinking Generalization
  • Generalization in Deep Learning
  • Slides
  • Boosting Slides pt. 2
  • Th Oct 8 Generalization of DNN cont.
  • A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
  • Stronger Generalization Bounds for Deep Nets via a Compression Approach
  • Tu Oct 13 Generalization of DNN cont.
    Th Oct 15 Generalization of DNN cont. Slides
    Tu Oct 20 Defending Against Adversarial Pertubations Slides
    Th Oct 22 cont.
    Tu Oct 27 Graphical Models Intro to Graphical Models:
  • Bishop Chapter 8
  • Murphy Chapter 19
  • Slides
    Th Oct 29 Latent Variable Models
  • Full Slides
  • Slides part 1
  • Tu Nov 5 cont. Slides part 2
    Th Nov 10 cont. Slides part 3
    Th Nov 12 cont. Slides part 4
    Tu Nov 17 Intro Reinforcement Learning RL Reference Book Slides
    Th Nov 19 RL: TD Methods Slides
    Tu Nov 24 RL: Function Approximators Slides
    Tu Dec 1 RL: Actor-Critic and Intro to DRL Slides
    Th Dec 3 DRL Slides
    Tu Dec 8 Final project presentation
    Th Dec 10 Final project presentation

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