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

Administrivia Syllabus Piazza GradeSource


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.


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 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.


  • Instructor: Lawrence Saul
  • Teaching assistants (TAs):
    Shivani Agrawal (sha014eng.ucsd.edu)
    Mainak Biswas (mabiswaseng.ucsd.edu)
    Vamsi Cheekatimalla (vcheekateng.ucsd.edu)
    Zhanglong Ji (z1j1eng.ucsd.edu)
    Apurva Pathak (appathakeng.ucsd.edu)
    Xinyue Li (xil431eng.ucsd.edu)
  • Lectures:
    [A] Tue/Thu 11:00 am - 12:20 pm, PCYNH 106
    [B] Tue/Thu 12:30 pm - 13:50 pm, PCYNH 109
  • TA discussion sections:
    Friday 5:00-6:00 pm, Center 214 (Mainak)
    Monday 10:30-11:30 am, CSE 4140 (Vamsi)
    Monday 12:00-1:00 pm, Center 212 (Xinyue)
    Monday 5:00-6:00 pm, Center 216 (Shivani)
    Monday 6:00-7:00 pm, Center 216 (Apurva)
  • Instructor office hours: Fri 9-10 am, Mon 9-10 am, EBU-3B 3214
  • TA office hours in EBU-3B:
    Mon 8:00-9:00 am, B240A (Vamsi)
    Mon 6:00-7:00 pm, B260A (Shivani)
    Tue 3:00-4:00 pm, B240A (Apurva)
    Thu 9:30-10:30 am, B260A (Xinyue)
    Fri 6:00-7:00 pm, 4152 (Mainak)
  • Grading: best 7 of 8 homework assignments (56%), in-class final (44%).


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:


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.
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.
HW 4 out.
Thu Oct 20 EM algorithm: derivation, proof of convergence.
Tue Oct 25 Examples of EM; applications to language modeling. HW 4 due.
HW 5 out.
Thu Oct 27 Hidden Markov models, automatic speech recognition, Viterbi algorithm.
Tue Nov 01 Forward-backward algorithm, Gaussian mixture models, Kalman filters. HW 5 due.
HW 6 out.
Thu Nov 03 Reinforcement learning (RL), Markov decision processes.
Tue Nov 08 Policy evaluation, policy improvement. HW 6 due.
HW 7 out.
Thu Nov 10 Policy iteration, value iteration.
Tue Nov 15 Stochastic approximation theory, temporal difference prediction. HW 7 due.
HW 8 out.
Thu Nov 17 Q-learning, extensions of RL.
Tue Nov 22 Bonus topic (if time). HW 8 due.
Thu Nov 24 Thanksgiving Holiday
Tue Nov 29 Final exam: part one (in-class)
Thu Dec 01 Final exam: part two (in-class)