CS534: Machine Learning

Course Description

This course will present an introduction to algorithms for machine learning and data mining. These algorithms lie at the heart of many leading edge computer applications including optical character recognition, speech recognition, text mining, document classification, pattern recognition, computer intrusion detection, and information extraction from web pages. Every machine learning algorithm has both a computational aspect (how to compute the answer) and a statistical aspect (how to ensure that future predictions are accurate). Algorithms covered include linear classifiers (Gaussian maximum likelihood, Naive Bayes, and logistic regression) and non-linear classifiers (neural networks, decision trees, support-vector machines, nearest neighbor methods). The class will also introduce techniques for learning from sequential data and advanced ensemble methods such as bagging and boosting.

Prerequisites: CS515; basic knowledge of search algorithms, probability, statistics, calculus, linear algebra. 4 Units.

Class Hours: MWF 9:00-10:00 Bat 250
Office Hours: Thursdays 9:00-10:30 Dear 221C
Grader: Charles Parker


Duda, Hart, and Stork: Pattern Classification. Make sure your copy is not the first printing (or go to David Stork's web page and download the bug fixes).

Course Handouts


In this class, we will be using the WEKA package from The University of Waikato (Hamilton, New Zealand). This is a package of machine learning algorithms and data sets that is very easy to use and easy to extend. See the assignment for Homework 2 for information about how to use WEKA.

Homework Assignments

Solutions and course grades are available on the Blackboard System.

Please turn in all homework in two forms: (i) as hardcopy at the start of class and (ii) electronically via the ENGR homework system. (To submit electronically, first Login to the ENGR Teach site, and then click on the Submit Assignment item on the left side of the page.)

Viewgraphs for Lectures

Tom Dietterich, tgd@cs.orst.edu