CSE 250A. Principles of Artificial Intelligence:
Probabilistic Reasoning and Decision-Making

Subject
Prerequisites
Administrivia
Textbooks
Syllabus
Grades
Lectures

Subject

Probabilistic methods for reasoning and decision-making 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.

Prerequisites

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 high-level 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.

Administrivia

  • Professor: Lawrence Saul
  • Teaching assistants: Youngmin Cho and Vineet Kumar
  • Lectures: Tue/Thu 11:00 am - 12:20 pm, CSB 002.
  • Sections: Mon 3-4 pm, Center Hall 212.
  • Instructor office hours: Fri 10-11 am, EBU3B 3214.
  • TA office hours: Fri 12-1 pm, Mon 1-2 pm, EBU3B B250A.
  • Grading: homework (~25%), two in-class exams (~40%), final exam (~35%).

Textbooks

The course does not closely follow a particular text; the lectures are meant to be self-contained. Nevertheless, the following texts (though not required) may be useful as general references:

Syllabus

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, d-separation, 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. Expectation-Maximization (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 Forward-backward 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 Q-learning, extensions of RL.
Thu Dec 01 Course wrap-up, grab-bag Q/A, evaluations. HW 7 due.
Wed Dec 07 Final exam.