Supervised learning setup. LMS.
Logistic regression. Perceptron. Exponential family.
Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.
Support vector machines.
Model selection and feature selection.
Ensemble methods: Bagging, boosting.
Evaluating and debugging learning algorithms.
Learning theory. (3 classes)
Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
VC dimension. Worst case (online) learning.
Practical advice on how to use learning algorithms.
MDPs. Bellman equations.
Value iteration and policy iteration.
Linear quadratic regulation (LQR). LQG.
Q-learning. Value function approximation.
Policy search. Reinforce. POMDPs.
Dates for Assignments and Midterm
Assignment 1: Out 10/05. Due 10/19.
Assignment 2: Out 10/19. Due 11/02.
Midterm: 11/09 (6 PM - 9 PM) - Venue - To Be Announced
Assignment 3: Out 11/02. Due 11/16.
Assignment 4: Out 11/16. Due 12/07.
Dates for Project Related Submissions
Project Proposal: Due 10/21 at 11:59 PM.
Project Milestone: Due 11/18 at 05:00 PM.
Poster Session: 12/13 (08:30 AM - 11:30 AM)
Final Writeup: Due 12/16 at 11:59 PM. (No Late Days)
All assignments are due at 11:00 AM after the class on corresponding Wednesdays.
Project related submissions are due at the times specified above.
A maximum of three late days can be applied to any single assignment, project proposal, or project milestone.
Late days cannot be used for the poster and the final report.