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Course: STAT 24610=STAT 37500
Title: Pattern Recognition
Instructor(s): Tracy Ke
Course Assistant(s): Likai Chen, Haoyang Liu
Class Schedule: Sec 01: TR 10:30-11:50AM in Eckhart 133
Office Hours:  
Textbook(s): Bishop, Pattern Recognition and Machine Learning, 1st edition.
Description: This course treats statistical models and methods for pattern recognition and machine learning. Topics include a review of the multivariate normal distribution, graphical models, computational methods for inference in graphical models in particular the EM algorithm for mixture models and HMM’s, and the sum-product algorithm. Linear discriminative analysis and other discriminative methods, such as decision trees and SVM’s are covered as well.

Prerequisites: Linear algebra at the level of STAT 24300. Knowledge of probability and statistical estimation techniques (e.g., maximum likelihood and linear regression) at the level of STAT 24500.