CSE 250A. Principles of Artificial Intelligence: Probabilistic Reasoning and DecisionMaking
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.
Relation to other courses
CSE 150 covers largely the same material as CSE 250A, but at a slower pace and less advanced mathematical level. The homework assignments in CSE 250A are also longer and more challenging.
 Professor: Lawrence Saul
 Teaching assistants: Youngmin Cho and Vineet Kumar
 Lectures: Tue/Thu 11:00 am  12:20 pm, CSB 002.
 Sections: Mon 34 pm, Center Hall 212.
 Instructor office hours: Fri 1011 am, EBU3B 3214.
 TA office hours: Fri 121 pm, Mon 12 pm, EBU3B B250A.
 Grading: homework (~25%), two inclass exams (~40%), final exam (~35%).
The course does not closely follow a particular text; the lectures are meant to be selfcontained. Nevertheless, the following texts (though not required) may be useful as general references:
Thu Sep 22 
Administrivia and course overview. 

Tue Sep 27 
Modeling uncertainty, review of probability, explaining away. 
HW 1 out. 
Thu Sep 29 
Belief networks: from probabilities to graphs. 

Tue Oct 04 
Conditional independence, dseparation, polytrees. 
HW 1 due. HW 2 out.
handout

Thu Oct 06 
Algorithms for exact and approximate inference. 

Tue Oct 11 
Maximum likelihood estimation; Markov models of language; naive Bayes models of text. 
HW 2 due. HW 3 out. 
Thu Oct 13 
Linear and logistic regression. Numerical optimization. 

Tue Oct 18 
Latent variable modeling. ExpectationMaximization (EM) algorithm. Auxiliary functions. 
HW 3 due. 
Thu Oct 20 
EM algorithm: derivation, proof of convergence. 

Tue Oct 25 
Quiz #1 
HW 4 out.

Thu Oct 27 
Examples of EM; applications to language modeling. 

Tue Nov 01 
Hidden Markov models, automatic speech recognition, Viterbi algorithm. 
HW 4 due. HW 5 out.
handout 
Thu Nov 03 
Forwardbackward algorithm, Gaussian mixture models. 

Tue Nov 08 
Reinforcement learning (RL), Markov decision processes. 
HW 5 due. HW 6 out. 
Thu Nov 10 
Policy evaluation, policy improvement. 

Tue Nov 15 
Policy iteration, value iteration. 
HW 6 due.

Thu Nov 17 
Stochastic approximation theory, temporal difference prediction. 

Tue Nov 22 
Quiz #2 
HW 7 out. 
Thu Nov 24 
Thanksgiving: no class. 

Tue Nov 29 
Qlearning, extensions of RL. 

Thu Dec 01 
Course wrapup, grabbag Q/A, evaluations. 
HW 7 due. 
Wed Dec 07 
Final exam. 


