CSE 250A. Principles of Artificial Intelligence:

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 150a, 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 have already taken CSE 150a.
Thu Oct 01  Administrivia and course overview.  
Tue Oct 05  Modeling uncertainty, review of probability, explaining away.  HW 1 out. 
Thu Oct 08  Belief networks: from probabilities to graphs.  
Tue Oct 13  Representing conditional probability tables. Conditional independence and dseparation.  HW 1 due. HW 2 out. 
Thu Oct 15  Probabilistic inference in polytrees.  
Tue Oct 20  More algorithms for inference: node clustering, cutset conditioning, likelihood weighting.  HW 2 due. HW 3 out. 
Thu Oct 22  Markov Chain Monte Carlo algorithms for inference. Learning from complete data.  
Tue Oct 27  Maximum likelihood estimation. Markov models of language. Naive Bayes models of text.  HW 3 due. HW 4 out. 
Thu Oct 29  Linear regression and least squares. Detour on numerical optimization.  
Tue Nov 03  Logistic regression, gradient descent, Newton's method. Learning from incomplete data.  HW 4 due. HW 5 out. 
Thu Nov 05  EM algorithm for discrete belief networks: derivation and proof of convergence.  
Tue Nov 10  EM algorithms for word clustering and linear interpolation.  HW 5 due. HW 6 out. 
Thu Nov 12  EM algorithms for noisyOR and matrix completion. Discrete hidden Markov models.  
Tue Nov 17  Computing likelihoods and Viterbi paths in hidden Markov models.  HW 6 due. HW 7 out. 
Thu Nov 19  Forwardbackward algorithm in HMMs. Gaussian mixture models.  
Tue Nov 24  Linear dynamical systems. Reinforcement learning and Markov decision processes.  HW 7 due. HW 8 out. 
Thu Nov 26  Thanksgiving holiday.  
Tue Dec 01  State and action value functions, Bellman equations, policy evaluation, greedy policies.  HW 8 due. HW 9 out. 
Thu Dec 03  Policy improvement and policy iteration. Value iteration. Algorithm demos. 

Tue Dec 08  Convergence of value iteration. Modelfree algorithms. Temporal difference prediction.  
Thu Dec 10  Qlearning, RL in large state spaces. Bonus topics. Course wrapup. 
HW 9 due 
Sat Dec 12  Takehome final exam (released). 