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Courses I have taught (or will teach) are below:

SemCourseTitleTopics
F 2016 CMSC 421 Artificial Intelligence Reinforcement learning, belief networks
F 2016 CMSC 828 Reinforcement Learning Everything there is to know! :)
S 2016 CMSC 726 Machine Learning Advanced topic in machine learning theory and practice
F 2015 CMSC 422 Machine Learning Undergraduate, supervised, unsupervised and imitation learning
F 2014 CMSC 723 Computational Linguistics Morphology, syntax, semantics, discourse
S 2014 CMSC 818 Linguistic Prediction (aka "Bayes in the brain") understanding linguistic prediction: how the brain does it, and how to model it computationally
S 2013 CMSC 422 Machine Learning Undergraduate, supervised and unsupervised learning
S 2013 CMSC 828 CL Seminar in Bayesian Nonparametrics Nonparametric models, inference methods and applications
F 2012 CMSC 723 Computational Linguistics Morphology, syntax, semantics, discourse
S 2012 CMSC 421 Artificial Intelligence Reinforcement learning, belief networks
F 2011 CMSC 726 Machine Learning Supervised and unsupervised learning
F 2011 CMSC 828F Multilingual Modeling Modeling and statistics for multilingual analysis/processing
F 2010 CMSC 723 Computational Linguistics Syntax, semantics, discourse, learning
S 2010 CS 5300 Artificial Intelligence Reinforcement learning, belief networks
S 2010 CS 7941 Topics in ML Structured prediction
F 2009 CS 5350 Machine Learning Supervised and unsupervised learning
F 2009 CS 7941 Topics in ML TBD
M 2009 CS 7941 Topics in ML Assorted recent results
S 2009 CS 5300 Artificial Intelligence Reinforcement learning, belief networks
S 2009 CS 7941 Topics in ML Theoretical machine learning
F 2008 CS 5350 Machine Learning Supervised and unsupervised learning
F 2008 CS 7941 Topics in ML Graphical models
M 2008 CS 7941 Topics in ML Multitask learning
S 2008 CS 5350 Machine Learning Supervised and unsupervised learning, reinforcement learning
S 2008 CS 7941 Topics in ML Kernel methods
F 2007 CS 5964 Applications of NLP Sequence models of text, machine translation, automatic document summarization
F 2007 CS 7941 Topics in ML Non-parametric Bayesian methods
M 2007 CS 7941 Topics in ML Manifold learning
S 2007 CS 5350 Machine Learning Supervised and unsupervised learning, structured prediction, Bayesian learning


credits: design and font inspired by Seth Able's LoRD, some images converted to ANSI using ManyTools, original drawing of me by anonymous.
last updated on twenty four july, two thousand seventeen.