Index of class resources

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

• Course Syllabus
• First Homework, due Tuesday Jan. 18th and Prob. 2 clarification
• HW1 solutions (partial)
• Second Homework, due Tuesday Feb. 1st
• HW2 solutions
• Preliminary Project Proposals Thursday Jan. 20th
• Preliminary Midterm topics list Version 2
• Sample Questions
• Guidelines on preparing the project writeup
• Potentially useful links

Additional Readings

• 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
• A note on EM from the last time I taught 242
• Kevin Murphy's intro to graphical models
• Nir Friedman's intro to Bayesian Networks
• Heckerman's intro to Bayesian Networks
• 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"
• Burges's SVM tutorial
• Kaelbling/Littman/Moore Reinforcement Learning Survey
• Sutton and Barto's Reinforcement learning book

Class Newsgroup:

http://apps.soe.ucsc.edu/forums/index.php?bn=classes_cmps242winter05 - for announcements, general discussion, and help

Lecture times:

Tu,Th 2-3:45 in Social Sciences II room 159.N

Instructor:

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

Phone: 459-2016

Office: E2 room 345b

Instructor Office Hours:

Thursday 4-5 and by appointment