Announcements and New on the page: (Page updated: Friday, 05-Jan-2007 10:02:00 PST )

• You can now pick up your project reports, Thursday Jan 4 and Tuesday Jan 9 at 4:00 would be good times.
• Let me know if you are interested in selling your Alpaydin book, and I will put you in touch with an undergrad who needs it.

Index of class specific resources

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

• Course Syllabus (.pdf)
• Slides for chapter 1
• Slides for chapter 2
• Slides for Bayesian learning (ch 3-5) (modified)
• Slides on Monty Hall problem
• Slides on Naive Bayes and logistic Regression
• Slides on comparisons of algorithms
• Slides on BayesNets pdf ppt
• Slides on Decision Trees pdf
• Slides on Neural networks pdf
• Slides on Support Vector Machines pdf
• Slides on NN and instance based techniquess pdf
• Slides on Boosting pdf
• Slides on clustering pdf
• Slides on HMMs pdf
• Slides on On-line Algs pdf (revised 11/7)
• Slides on feature selection pdf
• Slides on project
• Some project ideas (Revised Oct. 11)
• Sample Exam (.pdf)
• Midterm topic list (.pdf)

Homeworks

• Reading Assignments (.txt) last change: Friday, 17-Nov-2006 13:31:19 PST
• Homework 1 (revised) last change: Tuesday, 03-Oct-2006 14:33:42 PDT revised solutions
• VC handout for problem 1
• iris2.arff last change: Tuesday, 03-Oct-2006 14:33:42 PDT
• Project proposals due Oct. 16! (see projects.pdf)
• Homework 2 (due Fri Oct 27) last change: Tuesday, 03-Oct-2006 14:33:42 PDT solutions
• Homework 3 (exercises) last change: Tuesday, 03-Oct-2006 14:33:42 PDT
• Project progress reports due Nov. 17 (see projects.pdf and project report guidelines pdf )
• Project early deadline: December 1 (BEFORE class, +5%). Normal deadline: December 6 at 3pm

Additional Readings

• Mitchell's "The Discipline of Machine Learning"
• 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
• 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
• L. R. Rabiner (1989). "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceedings of the IEEE, vol 77, no 2, 257--287.
• 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
• Sebastiani Text Classification Survey
• Sutton and Barto's Reinforcement learning book
• Xu and Wunsch "Survey of Clustering Algorithms" (has lots of references)

Other Information

• Potentially useful links
• Machine learning contest

Reading Assignments

• Reading Assignments (.txt) last change: Friday, 17-Nov-2006 13:31:19 PST

General Class Information

Class Newsgroup:

We now have a newsgroup - ucsc.class.cmps242 on the news.ucsc.edu server. See ITS's newsgroup information page

Lecture times:

MWF 3:30-4:40 in Baskin Engineering room 156

Instructor:

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

Phone: 459-2016

Office: E2 room 345b

Instructor Office Hours:

1:00-2:00 Mondays