CMPS 242, Machine Learning, Spring 2001

This course is about formalizing the "learning problem" and finding good algorithmic solutions to various learning problems.

Instructor:

Prof. David Helmbold dph@cse.ucsc.edu

Office: 319 Applied Sciences

Office Hours: TBD

Lecture times:

Monday, Wednesday 5:00 to 6:45 pm in Merrill Academy 003 (in Baskin Engineering 156 after the first class).

Text:

Machine Learning by Mitchell.

Papers to read, pointers, solutions:

01: Nick Littlestone's on-line learning/Winnow paper ( "Learning Quickly When ..." )

Take a look at the " Decision tree learning" AIexploratorium web site, off of ( http://www.aaai.org/Pathfinder/html/trees.html )

You might find the home page for Computational Learning Theory ( http://www.learningtheory.org/ )

Solutions to Homework 1 ( postscript ) ( pdf )

Solutions to Homework 3 ( postscript ) ( pdf )

Algebra in EM lecture ( postscript ) ( pdf )

Tom Dietterich's "Machine Learning Research: Four Current Directions" ( postscript )

Handouts:

01: Course Information ( postscript ) ( pdf )

02: Homework 1 ( postscript ) ( pdf )

03: Homework 2 ( postscript ) ( pdf )

03: Homework 3 ( postscript ) ( pdf )

04: Topics Covered in the course. ( postscript ) ( pdf )

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