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 27  Administrivia and course overview.  
Tue Oct 02  Modeling uncertainty, review of probability, explaining away.  HW 1 out. 
Thu Oct 04  Belief networks: from probabilities to graphs.  
Tue Oct 9  Conditional independence, dseparation, polytrees.  HW 1 due. HW 2 out. 
Thu Oct 11  Algorithms for exact and approximate inference.  
Tue Oct 16  Maximum likelihood estimation; Markov models of language; naive Bayes models of text.  HW 2 due. HW 3 out. 
Thu Oct 18  Linear and logistic regression. Numerical optimization.  
Tue Oct 23  Latent variable modeling. ExpectationMaximization (EM) algorithm. Auxiliary functions.  HW 3 due. HW 4 out. 
Thu Oct 27  EM algorithm: derivation, proof of convergence.  
Tue Oct 30  Examples of EM; applications to language modeling.  HW 4 due. HW 5 out. 
Thu Nov 01  Hidden Markov models, Viterbi algorithm.  
Tue Nov 06  Midterm, 810 pm, Center 115/119 [A/B]  
Thu Nov 08  Forwardbackward algorithm, Gaussian mixture models, Kalman filters.  HW 5 due. HW 6 out. 
Tue Nov 13  Reinforcement learning (RL), Markov decision processes.  HW 6 due. HW 7 out. 
Thu Nov 15  Policy evaluation, policy improvement.  
Tue Nov 20  Policy iteration, value iteration.  HW 7 due. HW 8 out. 
Thu Nov 22  Thanksgiving Holiday  
Tue Nov 27  Stochastic approximation theory, temporal difference prediction.  HW 8 due. HW 9 out. 
Thu Nov 29  Qlearning, extensions of RL.  
Tue Dec 04  Bonus topics or catchup.  
Thu Dec 06  Bonus topics or review.  HW 9 due 
Mon Dec 10  Final exam, 710 pm, rooms TBA. 