The main text for this class is:

Duda, R. O., P. E. Hart, and D. G. Stork. Pattern Classification. 2nd Edition. New York: John Wiley & Sons, USA, 2001.

Supplemental readings are listed below and are noted in the Calendar.

Breese & Ball Handout (for example of an application)

Independence Diagram handout (read this lightly now; we will probably revisit it later in the course as well)

Cowell article. (This goes into more on Bayes Nets than we will cover, but is a good introduction that goes beyond DHS. Please read pp. 9-18 and give at least a quick glance at the rest, so you'll know what other topics it covers for possible future reference.)

Belhumeur et al paper

Rabiner, and Juang. 6.1-6.5 and 6.

"Election Selection: Are we using the worst voting procedure?" Science News (2 Nov. 2002).

Guest lecture by Yuan Qi. Jordan, and Bishop. Chap. 14 In Kalman Filtering. Tom Minka's short paper relating this to HMM's.

Guest lecture by Ashish Kapoor. Muller et al. "An Introduction to Kernel Based Learning Algorithms." In IEEE Trans on Neural Networks.

Combined "final" lecture: Yuan Qi introduces Bayes Point Machines and Junction Trees (for more information see Chap. 16 of Jordan & Bishop's book) and also the Cowell article from Lecture 5. Finally, a wrap up with a brief course overview.

Project Presentations: Face and Music/Artist data sets.

Project Presentations: PAF and special topics.