Course Schedule

Weekday Regular Schedule

Group Type Hours Location
All Lecture Sunday 13:00-16:00 Orenstein 103

Detailed Schedule

Lecture Date Topics Slides Scribes
1 19/3 Introduction to the course. Kernel regression classifiers. Graphical Model intro Lecture Kernel Regression, Graphical Models Representation
2 26/3 Exact Inference in Graphical Models Lecture
3 2/4 Reinforcement Learning Lecture
4 23/4 Approximate inference in Graphical Models Exact Inference and Approximate Inference
5 30/4 Approximate inference in Graphical Models Sampling Methods
6 7/5 Parameter Estimation and Structured Prediction Slides on Structured Prediction Parameter Learning Pseudo Likelihood
7 14/5 Overview of deep learning (focus on supervised) Slides
8 21/5 Unsupervised Deep Learning Slides
9 28/5 Reinforcement Learning Policy Gradient Methods Policy Search Review , Sutton & Barto Book, Pieter Abbeel Tutorial, David Silver Tutorial
10 4/6 Reinforcement Learning Value Based Methods
11 11/6 Generalization Bounds Rademacher Complexity
12 16/6 Generalization Bounds
13 25/6 Project Presentation

Code for kernel regression: solver, RBF kernel

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