Meeting Schedule

Date	Topic	Homework	Material
8/25/09	Introduction	Read Chapter 1 Read NY Times Article on Statistics Try matlab or octave in your work environment	intro.pdf
8/27/09	Review of Background Material I	Read Chapter 2 (Skim 2.3.8, 2.5)	probreview.pdf
9/01/09	Review of Background Material II		distributions.pdf
9/03/09	Probability Distributions	Read Chapter 3 (skim 3.5, 3.6) Read Lasso Paper	optimization.pdf
9/08/09	Linear Models for Regression	Read Section 2.5 Read Smoothing by Local Regression: Principles and Methods	regression.pdf
9/10/09	Non-parametric methods	Read Chapter 4	regularization.pdf
9/15/09	Linear Models for Classification	HW2 assigned; due 9/24/09	hw1-comments nonparametric.pdf
9/17/09	Slack	Read Chapter 14	classifiers.pdf Perceptron Demo
9/22/09	Decision Trees and Boosting	Read Chapter 5 (skim 5.3.4-5.4.6, 5.6-5.7) Read Schapire's Boosting Overview (Note that this is in postscript. You may need to install ghostview from the resources page.)	hw1-2-comments.pdf dtrees.pdf
9/24/09	Neural Networks	Reinforcement Learning Survey by Kaelbling et al.	hw2-comments.pdf neuralnet.pdf
9/29/09	Decision Theory and MDPs	HW3 assigned; due 10/15/09
10/01/09	Slack		exam-tips.pdf mdp.pdf
10/06/09	Fall Break
10/08/09	Midterm
10/13/09	Reinforcement learning
10/15/09	Reinforcement Learning	Read LSPI Read linear models	rl.pdf
10/20/09	Reinforcement Learning	Read Andrew Ng's Computational Learning Theory Notes HW4 assigned; due 10/29/09	batchrl.pdf
10/22/09	Computational Learning Theory	Read Chapter 6 (skim 6.3, 6.4.3, 6.4.4, 6.4.6)	colt.pdf
10/27/09	Kernel Methods	Read Chapter 7 (focus on 7.1)	kernels.pdf
10/29/09	Sparse Kernel Machines		svm.pdf
11/3/09	SVMs continued	Read Chapter 12 (focus on 12.1) Read the classic eigenfaces paper HW5 assigned; due 11/12/09
11/5/09	Continuous Latent Variables	Read Chapter 8	pca.pdf
11/10/09	Graphical Models	Read Chapter 9 Read An Impossibility Theorem for Clustering	bnets.pdf
11/12/09	Mixture Models and EM	Read Chapter 13 (skim 13.2.6 - 13.3.4)	clusterem.pdf
11/17/09	Sequential Data (HMMs)	Read Rabiner HMM Tutorial HW6 assigned; due 12/03/09	hmm.pdf
11/19/09	More Graphical Models	Skim Chapter 10. Read Chapter 11 (skim 11.4-11.6)	graphicalmodels.pdf
11/24/09	Approximate Inference
11/26/09	Thanksgiving
12/01/09	Slack/optional material
12/03/09	slack/optional material
12/12/09	Final Exam 7:00 - 10:00 PM(!) in D106