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 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 already have taken the undergraduate offering from me or Professor Alvarado.


  • Instructor: Lawrence Saul

  • Teaching assistants:
    Hammad Abdullah Ayyubi (hayyubiucsd.edu)
    Sameeksha Khillan (skhillanucsd.edu
    Rohit Kumar (r2kumarucsd.edu)
    Shimin Lin (s6linucsd.edu)
    Piyush Tayal (ptayalucsd.edu)
    Priyan Vaithilingam (pvaithilucsd.edu)
    Vijay Viswanath (v1viswanucsd.edu)
    Xinghan Wang (x2wangucsd.edu)

  • Lectures:
    [A] Tue/Thu 3:30-4:50 pm, Solis 107
    [B] Tue/Thu 12:30-1:50 pm, HSS 130

  • Problem-solving sessions:
    Mon 8-00-8:50 pm, CSB-001
    Mon 9:00-9:50 pm, CSB-001
    Thu 6:00-6:50 pm, CSB-001
    Fri 5:00-5:50 pm, CSB-002

  • Instructor office hours:
    Tue/Thu 5-6 pm, in EBU-3B 3214

  • TA office hours:
    Mon 9-10 am, EBU-3B B270A (Rohit)
    Tue 9-10 am, EBU-3B B250A (Shimin)
    Tue 11:30 am - 12:30 pm, EBU-3B B250A (Priyan)
    Wed 5:30-6:30 pm, EBU-3B B275 (Sameeksha)
    Thu 9:30-10:30 am, EBU-3B B260A (Hammad)
    Thu 5-6 pm, EBU-3B B260A (Vijay)
    Fri 1-2 pm, EBU-3B B250A (Xinghan)
    Fri 4-5 pm, EBU-3B B260A (Piyush)

  • Grading:
    (40%) best 8 of 10 homework assignments
    (50%) final exam
    (10%) best of final or optional homeworks


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 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, d-separation, 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. Expectation-Maximization (EM) algorithm. Auxiliary functions. HW 3 due.
HW 4 out.
Thu Oct 24 EM algorithm: derivation, proof of convergence.
Tue Oct 29 Examples of EM: matrix factorization, mixture modeling. HW 4 due.
HW 5 out.
Thu Oct 31 Hidden Markov models, Viterbi algorithm.
Tue Nov 05 Forward-backward algorithm, Gaussian mixture models, Kalman filters. HW 5 due.
HW 6 out.
Thu Nov 07 Reinforcement learning (RL), Markov decision processes.
Tue Nov 12 Policy evaluation, policy improvement. HW 6 due.
HW 7 out.
Thu Nov 14 Policy iteration, value iteration.
Tue Nov 19 Stochastic approximation theory, temporal difference prediction. HW 7 due.
HW 8 out.
Thu Nov 21 Q-learning, extensions of RL.
Tue Nov 26 Bonus topics or catch-up. HW 8 due.
HW 9 out.
Thu Nov 28 Thanksgiving holiday
Tue Dec 03 Bonus topics or catch-up.
Thu Dec 05 Bonus topics or review. HW 9 due
Sat Dec 07 Final exam, 3-6 pm, rooms TBA.