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schedule - z Reinforcement learning and control. (4...

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z Introduction (1 class) Basic concepts. z Supervised learning. (6 classes) 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, ECOC. z Learning theory. (3 classes) Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds. VC dimension. Worst case (online) learning. Advice on using learning algorithms. z Unsupervised learning. (5 classes) Clustering. K-means. EM. Mixture of Gaussians. Factor analysis. PCA. MDS. pPCA. Independent components analysis (ICA).
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Unformatted text preview: z Reinforcement learning and control. (4 classes) MDPs. Bellman equations. Value iteration. Policy iteration. Linear quadratic regulation (LQR). LQG. Q-learning. Value function approximation. Policy search. Reinforce. POMDPs. z Assignment 1: Out 10/3. Due 10/17. z Assignment 2: Out 10/17. Due 10/31. z Assignment 3: Out 10/31. Due 11/14. z Assignment 4: Out 11/14. Due 12/3. z Term project: Proposals due 10/19. Milestone due 11/16. Poster presentations on 12/12; final writeup due on 12/14 (no late days). CS 229 Machine Learning Handout #2: Tentative Course Schedule Syllabus Dates for assignments...
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