CPS 271: Numeric Artificial Intelligence (Machine Learning)
Fall 2005

Course information
Schedule
Links


Date Topic Homework Handouts and slides Optional Readings
8/30/05 Introduction Review basic stats and probability!! Intro: ps
9/1/05 Review of Probability required reading:
Andrew Ng's Lecture Notes on Supervised learning		 Probabilities: ps #1 ps #2 Bishop chapter 1
Hastie et al. chapters 1-3.2
Mitchell chapter 1
9/6/05 Regression Homework 1 Regression Slides Regression Demo
9/8/05 Classification required reading:
Andrew Ng's Lecture Notes on Generative Algorithms		 Classification Slides Bishop chapters 2-3
Hastie et al. chapter 4
Perceptron Demo
9/13/05 Classification Via Density Estimation Density Estimation for Classification Slides Mitchell chapter 6.1-6.10
9/15/05 Neural Nets Neural Networks Slides Bishop chapters 3-4
Mitchell chapters 3-4
Russell and Norvig 18.3 and 20.5
9/20/05 Decision Trees & Instance Based (non-parameteric) Methods Homework 2 Decision Tree Slides
Instance Based Methods (part I) slides		 Mitchell chapter 8
9/22/05 Continue Instance Based Methods & Learning Theory required reading:
Andrew Ng's Lecture Notes on Learning Theory		 Instance Based Methods II
Learning Theory
9/27/05 Boosting I Boosting Introduction Boosting Slides Boosting Web Page
A Less Brief Introduction to Boosting
Adaboost Applet
9/29/05 Boosting II
10/4/05 Support Vector Machines I required reading:
Andrew Ng's Lecture Notes on Support Vector Machines
Homework 3
Project Description		 SVM slides Hearst (Ed.) SVM Overview
Burges SVM Tutorial
Kernel Machines web Page
SVM Applet
Another SVM applet
10/6/05 Support Vector Machines II
10/11/05 Review
10/13/05 Fall Break
10/18/05 Midterm
10/20/05 Good Practices required reading:
Andrew Ng's Lecture Notes on Regularization and Model Selection		 slides
10/25/05 Reinforcement Learning Intro Andrew Ng's RL Introduction
Kaelbling et al. RL survey (skim)
slides Sutton & Barto RL Book
Russell & Norvig Chapters 17 and 21
Mitchell Chapter 13
10/27/05 RL Intro Continues slides pathlearner
11/1/05 Least Squares Policy Iteration Least squares Policy Iteration
Homework 4		 slides
11/3/05 Policy Search Approximate Planning in Large POMDPs via Reusable Trajectories
PEGASUS		 slides
11/8/05 (Catch up)
11/10/05 Clustering I Andrew Ng's Lecture Notes on Clustering slides K-means Demo
11/15/05 Clustering and EM Andrew Ng's Notes on EM slides EM Demo
11/17/05 Dimensionality Reduction Andrew Ng's Notes on PCA
Eigenface Paper		 slides
11/22/05 Bayes Nets (no required reading)
homework 5		 slides Belief Net Inference Procedural Guide
11/29/05 HMMs Rabiner HMM Tutorial slides
12/1/05 Learning in HMMs and Bayes nets slides Kevin Murphy's Bayes net intro