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.
Final exam: 24 hour take-home exam, Days: June 3-4 or June 4-5, Time: 5pm start, 5pm end. (students can pick start day)
All assignments and project related submissions are due at 11pm on the corresponding date.
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, the final report, or the take-home exam.