Announcements and New on the page: (Page updated: Tuesday, 02-Mar-2010 09:04:33 PST )

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

Handouts

• Course Syllabus (.pdf)
• VC Dimension notes (.pdf)
• Back Propagation notes (.pdf)
• LeastSquares as Max Likelihood and Bias-Variance (.pdf)
• Midterm topic list (.pdf)
• Some midterm Sample Questions (.pdf)

Homeworks

• Homework 1, due Thursday Jan 21 (.pdf) solutions
• Homework 2, due Thursday Jan 28 (.pdf) solutions
• Homework 3, due Thursday Feb 4 (.pdf) solutions
• diabetes.arff datafile (.pdf)
• Information on Project Proposals and Reports (.pdf)
• Project Proposals due Thursday Feb. 4
• Homework 4, due Tuesday Feb 16 (.pdf) solutions
• Project Progress Reports due Thursday Feb. 25 (at Exam)

Reading Assignments

See Reading Assignments (.txt) Changed: Thursday, 11-Feb-2010 12:02:46 PST

Assigned papers:
• Mitchell's "The Discipline of Machine Learning"
• Schapire's "The Boosting Approach to Machine Learning:An Overview"

Lecture slides

• slides

Additional Readings

• Burges SVM tutorial
• Avrim Blum's On-line Learning Survey
• Tips for Presenters

General Class Information

Class Newsgroup:

(none at this time)

Lecture times:

TuTh 10-11:45 in Engineering2 room 194

Instructor:

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

Phone: 459-2016

Office: E2 room 345b

Instructor Office Hours:

Tuesdays from 1:00 to 3:00 and by appointment.

TA

Name: Dimitrios Skourtis (skourtis@soe.ucsc.edu)

Office hours: Fridays from 12:00 to 1:00 in BE 358.