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:
    Saicharan Duppati (sduppatieng.ucsd.edu)
    Ishan Gupta (i2guptaeng.ucsd.edu)
    Shubham Gupta (shg061eng.ucsd.edu)
    Vikas Sakaray (vsakarayeng.ucsd.edu)
    Induja Sreekanthan (isreekaneng.ucsd.edu)
    Nivetha Thiruverahan (nthiruveeng.ucsd.edu)
    Dustin Wright (dbw003eng.ucsd.edu)

  • Lectures:
    [A] Tue/Thu 9:30 am - 10:50 am, WLH 2005
    [B] Tue/Thu 12:30 pm - 13:50 pm, Solis 104

  • Discussion sections:
    Mon 09:00-09:50AM, CSB 005 (Sai)
    Mon 03:00-03:50PM, WLH 2111 (Induja)
    Thu 05:00-05:50PM, PETER 102 (Shubham)
    Fri 12:00-12:50PM, PCYNH 120 (Vikas)
    Fri 04:00-04:50PM, PCYNH 120 (Nivetha)

  • Instructor office hours:
    Mon/Fri 10-11 am, in EBU-3B 3214

  • TA office hours:
    Mon 1:00-2:00 pm, CSE 4109 (Shubham)
    Mon 4:30-5:30 pm, CSE 3217 (Sai)
    Wed 3:30-4:30 pm, CSE B240A (Vikas)
    Wed 5:00-6:00 pm, CSE B260A (Dustin)
    Thu 3:30-4:30 pm, CSE 3217 (Nivetha)
    Fri 9:00-10:00 am, CSE B250A (Ishan)
    Fri 11:00 am - noon, CSE B270A (Induja)

  • Grading:
    (50%) best 8 of 9 homework assignments
    (50%) final exam


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 28 Administrivia and course overview.
Tue Oct 03 Modeling uncertainty, review of probability, explaining away. HW 1 out.
Thu Oct 05 Belief networks: from probabilities to graphs.
Tue Oct 10 Conditional independence, d-separation, polytrees. HW 1 due.
HW 2 out.
Thu Oct 12 Algorithms for exact and approximate inference.
Tue Oct 17 Maximum likelihood estimation; Markov models of language; naive Bayes models of text. HW 2 due.
HW 3 out.
Thu Oct 19 Linear and logistic regression. Numerical optimization.
Tue Oct 24 Latent variable modeling. Expectation-Maximization (EM) algorithm. Auxiliary functions. HW 3 due.
HW 4 out.
Thu Oct 26 EM algorithm: derivation, proof of convergence.
Tue Oct 31 Examples of EM; applications to language modeling. HW 4 due.
HW 5 out.
Thu Nov 02 Hidden Markov models, Viterbi algorithm.
Tue Nov 07 Forward-backward algorithm, Gaussian mixture models, Kalman filters. HW 5 due.
HW 6 out.
Thu Nov 09 Reinforcement learning (RL), Markov decision processes.
Tue Nov 14 Policy evaluation, policy improvement. HW 6 due.
HW 7 out.
Thu Nov 16 Policy iteration, value iteration.
Tue Nov 21 Stochastic approximation theory, temporal difference prediction. HW 7 due.
HW 8 out.
Thu Nov 23 Thanksgiving Holiday
Tue Nov 28 Q-learning, extensions of RL. HW 8 due.
HW 9 out.
Thu Nov 30 Bonus topics.
Tue Dec 05 Bonus topics.
Thu Dec 07 Bonus topics. HW 9 due
Thu Dec 14 Final exam.