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Teaching

Statistical Inference with Engineering Applications (Harvard)

Semester: 

Fall

Offered: 

2013
http://isites.harvard.edu/course/colgsas-9816

Statistical decision theory; hypothesis testing; linear and non-linear estimation; maximum likelihood and Bayes approaches; graphical models and message passing algorithms; large deviation analysis and asymptotic methods in statistics; stochastic processes and systems; Wiener and Kalman filtering; Markov chain Monte-Carlo methods; applications to physical, chemical, biological and information systems.

Linear Systems Fundamentals (UC San Diego)

Semester: 

Summer

Offered: 

2011
http://videoprocessing.ucsd.edu/~stanleychan/class/Su11/ece101.html

Complex variables. Singularities and residues. Signal and system analysis in continuous and discrete time. Fourier series and transforms. Laplace and z-transforms. Linear Time Invariant Systems. Impulse response, frequency response, and transfer functions. Poles and zeros. Stability. Convolution. Sampling. Aliasing.

This course is a fundamental signal processing course designed for Junior/ Senior students majoring in Electrical Engineering. I taught this course during Summer 2011 in the UC San Diego, under the Graduate Teaching Fellowship Award.