CSE 250A. Principles of Artificial Intelligence:

Administrivia  Syllabus  Piazza  GradeSource 
Probabilistic methods for reasoning and decisionmaking under uncertainty. Topics include: inference and learning in directed probabilistic graphical models; prediction and planning in Markov decision processes; applications to computer vision, robotics, speech recognition, natural language processing, and information retrieval.
The course is aimed broadly at advanced undergraduates and beginning graduate students in mathematics, science, and engineering. Prerequisites are elementary probability, multivariable calculus, linear algebra, and basic programming ability in some highlevel language such as C, Java, or Matlab. Programming assignments are completed in the language of the student's choice.
CSE 250a covers largely the same topics as CSE 150 (as I teach it), but at a faster pace and more advanced mathematical level. The homework assignments and exams in CSE 250A are also longer and more challenging. In general you should not take CSE 250a if you already have taken CSE 150 from me in a previous quarter.
Thu Sep 24  Administrivia and course overview.  
Tue Sep 29  Modeling uncertainty, review of probability, explaining away.  HW 1 out. 
Thu Oct 01  Belief networks: from probabilities to graphs.  
Tue Oct 06  Conditional independence, dseparation, polytrees.  HW 1 due. HW 2 out. 
Thu Oct 08  Algorithms for exact and approximate inference.  
Tue Oct 13  Maximum likelihood estimation; Markov models of language; naive Bayes models of text.  HW 2 due. HW 3 out. 
Thu Oct 15  Linear and logistic regression. Numerical optimization.  
Tue Oct 20  Latent variable modeling. ExpectationMaximization (EM) algorithm. Auxiliary functions.  HW 3 due. 
Thu Oct 22  EM algorithm: derivation, proof of convergence.  
Tue Oct 27  Quiz #1  HW 4 out. 
Thu Oct 29  Examples of EM; applications to language modeling.  
Tue Nov 03  Hidden Markov models, automatic speech recognition, Viterbi algorithm.  HW 4 due. HW 5 out. 
Thu Nov 05  Forwardbackward algorithm, Gaussian mixture models, Kalman filters.  
Tue Nov 10  Reinforcement learning (RL), Markov decision processes.  HW 5 due. HW 6 out. 
Thu Nov 12  Policy evaluation, policy improvement.  
Tue Nov 17  Policy iteration, value iteration.  HW 6 due. 
Thu Nov 19  Stochastic approximation theory, temporal difference prediction.  
Tue Nov 24  Quiz #2  HW 7 out. 
Thu Nov 26  Thanksgiving Holiday  
Tue Dec 01  Qlearning, extensions of RL.  
Thu Dec 03  Bonus topic (if time).  HW 7 due. 
Fri Dec 11  Final exam. 