This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.
As the textbook for the course, we will be using the 2nd edition of Pattern Classification by Duda, Hart & Stork. This book has now been published and is available at book stores (e.g. amazon.com and bn.com). Besides the text, we will also make use of articles from the literature which will be made available from the resources page as the course progresses.
This Year's Course
Fall 2002 web page
Fall 2001 web page
Fall 1999 web page