Teaching Courses

My recipe for teaching advanced topics in Computer vision, Pattern Recognition and Machine Learning: I divide the literature into three methods 1. Descriptive, 2. Generative, and 3. discriminative. Each method include two aspects: a. representation, and b. computation. Thus things fall into a 2x3 table. I organize them in three classes above.

	descriptive	generative	discriminative
representation	Stat232A	Stat232A	Stat231-CS276A
computation	Stat232B	Stat232B	Stat231-CS276A

   University of  California, Los Angeles:

    Stat 13     Introduction to Statistical Methods for the Life and Health Sciences
    Stat 161    Introduction to Pattern Recognition and Machine Learning
    Stat 231:   Pattern Recognition and Machine Learning (Cross listing with CS 276A)
    Stat 232A:  Statistical Modeling and Learning in Vision and Image Science,
    Stat 232B:  Statistical Computing and Inference in Vision and Image Science

        

   Ohio State University

    cis 630:    Survey of Artificial Intelligence I
    cis 730:    Survey of Artificial Intelligence II
    cis 788:    Pattern Recognition and Machine Intelligence
    cis 788:    Advanced Topics in Computer Vision --- MRF and MCMC
    cis 788:    Advanced Topics in Computer Vision

                 

   Stanford University:
     cs329b:   Pattern Recognition and Machine Intelligence
     cs232c:   Statistical and Computational Theories of Vision