2017-2018 Academic Year
2017, Fall, UCSD Cogs 181, Neual Networks and Deep Learning (undergraduate)

2015-2016 Academic Year
2017, Sppring, UCSD Cogs 260: Image Recognition (graduate)
2017, Spring, UCSD Cogs 118A, Introduction to machine learning I (undergrduate)
2017, Winter, UCSD Cogs 181, Neural Networks and Deep Learning (undergradate): an introductory course to neural networks.


2015-2016 Academic Year:
2015 Fall, UCSD Cogs 109,  Modeling and Data Analysis (undergraduate): a core computation course in cognitive science
2016 Winter, UCSD Cogs 118A, Natural Computation I (undergradate): an introductory course to machine learning.
2016 Spring, UCSD Cogs 260, Image Recognition (a big data course for graduate students in social sciences)
2016 Spring, UCSD Cogs 185 Advanced Machine Learning Methods (undergraduate)


2014-2015 Academic Year:
2014 Fall, UCSD Cogs 1, Introduction to Cognitive Science (undergraduate)
2015 Winter, UCSD Cogs 118A, Natural Computation I (undergradate): an introductory course to machine learning.
2015 Spring, UCSD Cogs 225, Visual Computing (graduate): about low-, mid-, and high-level applications in computer vision.
2015 Spring, UCSD Cogs 185 Advanced Machine Learning Methods (undergraduate)


2013-2014 Academic Year:
2014 Winter, UCSD Cogs 118A Natural Computation (undergraduate)
2014 Winter, UCSD Cogs 260 Visual Computing (graduate)
2014 Spring, UCSD Cogs 185 Advanced Machine Learning Methods (undergraduate)


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2013 Spring, UCLA CS 269, Advanced Machine Learning

2012 Fall, University of Huazhong Science and Technology University, Computer Vision and Medical Imaging

2010 Spring, UCLA CS 269, Topics in Advanced Machine Learning
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2009 Fall, Tutorial: Discriminative Learning for Medical Imaging, MICCAI 2009

2009 Summer:  Lectures on Sino-USA Summer School in Vision, Learning, and Pattern Recognition
Ensemble discriminative learning
Supervised and weakly-supervised learning

2005 Fall, MCMC Short Scourse, ICCV 2005
Outline
Lecture 1, Introduction to Markov chain Monte Carlo (Song-Chun Zhu)
Lecture 2, Two basic designing tools (Frank Delleart)
Lecture 3,  A variety of tricks for MCMC design  (Song-Chun Zhu)
Lecture 4, Reversible jumps (Zhuowen Tu)
Lecture 5, Data-driven Markov chain Monte Carlo (Frank Delleart)
Lecture 6, Cluster sampling (Song-Chun Zhu)
Lecture 7, Exact sampling techniques (Zhuowen Tu)