CSE 583 / EE 552 Syllabus (Fall 2008)

This is a tentative syllabus. Slides, handouts, and updates will be available through the course web page.

Week of	Tue	Thu
Aug 26	Introduction (probability theory, decision theory, matlab) Reading: Ch1 Matlab tutorial	Introduction (Cont) Reading: Ch2
Sept 2	Linear Regression Reading: Ch3	Linear Classification and Project 1 Reading: Ch4
Sept 9	Linear Classification Reading: Ch4	Linear Classification Reading: Ch4 Project 1 Due
Sept 16	Linear Classification Reading: Ch4	Non-parametric methods (probablity density estimation) Reading: Ch2
Sept 23	Feature Selection and and Project 2 Reading: TBA	Principal Component Analysis Reading: Ch12
Sep 30	Dimension Reduction locally linear embedding	Kernel Methods and Project 3 Reading: Ch6 Project 2 Due
Oct 7	Support Vector Machines Reading: Ch7	Support Vector Machines (Cont.) and Project 4 (Final project) Reading: Ch7 Project 3 Due
Oct 14	Case study (projects 1-3)	Clustering Reading: Ch9
Oct 21	EM Reading: Ch9	Graphical Models Reading: Ch8 Course Project Proposal Due
Oct 28	Graphical Models (Cont.) Reading: Ch8	Markov Random Fields Reading: Ch8 Project update
Nov 4	Belief Propogation Reading: Ch8	Hidden Markov Models Ch13
Nov 11	Sampling Methods Reading: Ch11	MCMC Reading: Ch11
Nov 18	Guest Lecture (TBA)	TBA
Nov 25	THANKSGIVING BREAK (NO CLASS)	--
Dec 2	Advanced Topics: Combining Models Reading: Ch13,14	Variational Inference Reading: Ch10
Dec 9	TBA	Last Class: Project Presentation
Dec 19	--	Final project writeups (hard copy) due, Friday Dec 19