CSE 250A. Principles of Artificial Intelligence:

Subject Prerequisites Administrivia Textbooks Syllabus Grades Lectures 
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 150 covers largely the same material as CSE 250A, but at a slower pace and less advanced mathematical level. The homework assignments (and exams) in CSE 250A are longer and more challenging.
Thu Sep 26  Administrivia and course overview.  
Tue Oct 01  Modeling uncertainty, review of probability, explaining away.  HW 1 out. 
Thu Oct 03  Belief networks: from probabilities to graphs.  
Tue Oct 08  Conditional independence, dseparation, polytrees.  HW 1 due. HW 2 out. 
Thu Oct 10  Algorithms for exact and approximate inference.  
Tue Oct 15  Maximum likelihood estimation; Markov models of language; naive Bayes models of text.  HW 2 due. HW 3 out. 
Thu Oct 17  Linear and logistic regression. Numerical optimization.  
Tue Oct 22  Latent variable modeling. ExpectationMaximization (EM) algorithm. Auxiliary functions.  HW 3 due. 
Thu Oct 24  EM algorithm: derivation, proof of convergence.  
Tue Oct 29  Quiz #1  HW 4 out. 
Thu Oct 31  Examples of EM; applications to language modeling.  
Tue Nov 05  Hidden Markov models, automatic speech recognition, Viterbi algorithm.  HW 4 due. HW 5 out. 
Thu Nov 07  Forwardbackward algorithm, Gaussian mixture models.  
Tue Nov 12  Reinforcement learning (RL), Markov decision processes.  HW 5 due. HW 6 out. 
Thu Nov 14  Policy evaluation, policy improvement.  
Tue Nov 19  Policy iteration, value iteration.  HW 6 due. 
Thu Nov 21  Stochastic approximation theory, temporal difference prediction.  
Tue Nov 26  Quiz #2  HW 7 out. 
Thu Nov 28  Thanksgiving: no class.  
Tue Dec 03  Qlearning, extensions of RL.  
Thu Dec 05  Course wrapup, grabbag Q/A, evaluations.  HW 7 due. 
Fri Dec 13  Final exam. 