CS545 Machine Learning Fall 2009 Department of Computer Science

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CS545 Overview

"Making the simple complicated is commonplace; making the complicated simple, awesomely simple, that's creativity." -- Charles Mingus

Prerequisites: Programming experience with at least two languages, courses or experience in linear algebra and statistics. Experience with programming regression, classification, neural networks, or other adaptive algorithms is a good background for this class. Consult the instructor to determine if you are sufficiently prepared.

Textbook:

• Required:
  • Pattern Recognition and Machine Learning, by Christopher M. Bishop, published by Springer (2007).
• Suggested supplementary books. These are not necessary, because there are very good on-line tutorials and other resources available.
  • Data Analysis and Graphics Using R - An Example-Based Approach, by John Maindonald and John Braun, published by Cambridge University Press, 2nd edition, 2007. Click on the title to go to author's web site for lots of R code examples. Also find the book here on amazon.com.
  • R Graphics, by Paul Murrell, published by Chapman & Hall, 2005.
  • Linear Models with R, by Julian Faraway.
  • S Programming, by William N. Venables, Brian D. Ripley
  • Modern Applied Statistics with S, Fourth Edition, by W. N. Venables and B.D. Ripley. It is available here at Google books.

Time and Place: Tuesday, Thursday, 12:30 - 1:45 PM, in Natural Environmental Sciences B101

Description: In this class you will learn about a variety of approaches to using a computer to discover patterns in data. The approaches include techniques from statistics, linear algebra, and artificial intelligence. Students will be required to solve written exercises, implement a number of machine learning algorithms and apply them to sets of data, and hand in written reports describing the results.

For implementations, we will be using R, an open-source environment very similar to S and S-Plus. You may download and install R on your computer, and work through the on-line tutorials to help prepare for this course.

Class meetings will be a combination of lectures by the instructor, discussions of students' questions, and some student presentations in class.

A lot of material will be covered in this course. Students are expected to speak up in class with questions and observations they have about the material. Do not expect to be able to complete all assignments working on your own and not asking any questions. If you find yourself wondering what the next step is in finishing an assignment, please feel free to call or e-mail the instructor. You may also discuss assignments with other students, but your code and report must be written by you. You are expected to be familiar with the CS Department policy on cheating.

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