CPS 271: Machine Learning
Fall 2007

Course information
Schedule
Links


Date Topic Homework Handouts and slides
8/28/07 Introduction Read Chapter 1 intro.pdf
8/30/07 Review of Background Material Read Chapter 2 basicprob.pdf
choosingpredictions.pdf
9/04/07 Review continued HW1 Assigned
9/06/07 Probability Distributions Read Chapter 3 distributions.pdf
9/11/07 Linear Models for Regression Read Chapter 4 regression.pdf regression demo
Andrew Moore's notes on cross validation
9/13/07 Linear Models for Classification Read Chapter 5
HW1 Due		 linclass.pdf perceptron demo
9/18/07 Neural Nets Read Andrew Ng notes on Computational Learning Theory
HW2 Assigned		 nn.pdf
9/20/07 Catch up
9/25/07 Computational Learning Theory Read Chapter 6 colt.pdf
9/27/07 Kernel Methods Read Chapter 7
HW2 Due		 kernels.pdf
10/2/07 Non-parametric Methods Read Chapter 8
HW3 Assigned
Project Assigned		 nonparametric.pdf
10/4/07 Sparse Kernel Machines svm.pdf SVM Applet
Hearst(Ed.)SVM Overview
Burges SVM Tutorial
10/9/07 Fall Break
10/11/07 Midterm
10/16/07 Midterm Therapy
HW 3 Due		 How to survive a math heavy exam
10/18/07 SVMs Continues
10/23/07 Graphical Models Read Chapter 9
HW4 Assigned		 bnets.pdf Kevin Murphy Bayes Net Intro
10/25/07 Mixture Models and EM Read Chapter 10 EM.pdf K means Demo
10/30/07 Catch up gmodels.pdf
11/1/07 Approximate Inference Read Chapter 11
HW 4 Due		 dai.pdf
11/06/07 Sampling Methods Read Chapter 12 ivs.pdf
11/08/07 Continuous Latent Variables Read Chapter 13
HW5 Assigned		 pca.pdf Eigenface Paper
Eigenfaces demo page
11/13/07 Sequential Data (HMMs) Reinforcement Learning Survey beyondpca.pdf
hmms.pdf		 Rabiner HMM Tutorial
11/15/07 HMMs continued and Graphical Model Learning RL reading gmlearning.pdf
11/20/07 MDPs Least Squares Policy Iteration
HW 5 Due		 mdp.pdf
11/22/07 Thanksgiving Break
11/27/07 Reinforcement Learning lspi.pdf Sutton and Barto RL Book
TD-Gammon
11/29/07 Review for final