Announcements and New on the page: (Page updated: Wednesday, 30-Nov-2005 15:59:47 PST )

• Project reports due Monday December 5 at 4:30 pm in my office
• Someone left a white sweater in my office - you can pick it up until 4:15 today or beore/after class Monday



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The text for the class is Introduction to Machine Learning by Alpaydin.

Index of class resources

Handouts — homework problem sets, homework solutions, other helpful handouts
General Class Information — class and section times, instructor and TA information

Homework and Handouts

• Course Syllabus (.ps) (.pdf)
• Reading Assignments (.txt) last change: Friday, 18-Nov-2005 13:23:18 PST
• Homework 1 (.pdf) due Friday Oct. 7
• Second Homework, due Friday Oct. 21
• Preliminary Project Proposals due Wednesday October 26th
• Past Projects and Project Ideas and Text mining project ideas and Final Project Report Guidlines
• Sample Questions
• HW1 solutions
• HW2 solutions
• Midterm topics list
• Sample project 1 and Sample project 2 and Sample project 3

Additional Readings

• An Introduction to Variable and Feature Selection by Guyon and Elisseeff.
• Bilmes, J. A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical Report, University of Berkeley, ICSI-TR-97-021, 1997. also at http://citeseer.ist.psu.edu/bilmes98gentle.html local copy
• A note on EM (updated)
• Kevin Murphy's intro to graphical models
• Bayesian Networks Without Tears by Charniak
• Heckerman's intro to Bayesian Networks
• Nir Friedman's intro to Bayesian Networks
• Burges's SVM tutorial
• Robert Schapire's MSRI boosting overview paper (.ps)
• Avrim Blum's on-line learning survey
• Manfred's on-line learning tutorial
• Avrim Blum's learning tutorial
• Nick Littlestone's Winnow paper (Learning Quickly When Irrelevant Attributes Abound...)
• Littlestone and Warmuth "The Weighted Majority Algorithm"
• Kaelbling/Littman/Moore Reinforcement Learning Survey
• Sutton and Barto's Reinforcement learning book
• A survey on clustering with lots of references

Other Information

• Potentially useful links
• Machine learning contest

Reading Assignments

• See link under handouts

General Class Information

Class Newsgroup:

We now have a newsgroup - ucsc.class.cmps242 on the news.ucsc.edu server.

Lecture times:

MWF 2-3:10 in Social Sciences II room 167

Instructor:

Name: David Helmbold (dph@cse.ucsc.edu)

Phone: 459-2016

Office: E2 room 345b

Instructor Office Hours:

MW 3:15-4 and by appointment