Many of the courses listed below have useful reference materials, software, and problem sets posted online. I have also prepared several talks and tutorials focusing on advanced topics in machine learning and computer vision.
Brown University
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CSCI1950-F: Introduction to Machine Learning (Spring 2012)
An introduction to the theory and practice of statistical machine learning.
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CSCI2950-P: Applied Bayesian Nonparametrics (Fall 2011)
A survey of classical and contemporary Bayesian nonparametric research.
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CSCI1950-F: Introduction to Machine Learning (Spring 2011)
An introduction to the theory and practice of statistical machine learning.
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CSCI2950-P: Learning & Inference in Probabilistic Graphical Models (Spring 2010)
A survey of state-of-the-art variational and Monte Carlo methods for statistical learning and inference in graphical models.
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CSCI1950-F: Introduction to Machine Learning (Fall 2009)
An introduction to the theory and practice of statistical machine learning, co-taught with Mark Johnson.
University of California, Berkeley
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CS294: Practical Machine Learning (Spring 2008)
I taught a guest lecture on hidden Markov models and graphical models in this applied introduction to statistical machine learning.
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CS294: Practical Machine Learning (Fall 2006)
Earlier edition, in which my lecture focused on hidden Markov models.
Massachusetts Institute of Technology
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6.432: Stochastic Processes, Detection, & Estimation (Spring 2004)
As a teaching assistant for this graduate course in statistical signal processing, I taught weekly, theoretically oriented recitations.
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6.454: Graduate Seminar in Area I (Fall 2003)
Prof. David Forney, Constantine Caramanis, and I co-organized this graduate seminar on advanced topics in communication, control, and signal processing. The webpage contains tutorial summaries for many interesting topics.
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6.801/6.866: Machine Vision (Fall 2002)
As a teaching assistant, I helped develop course materials focusing on applications of machine learning in computer vision.
