CSE 150 - Winter 2014
Introduction to Artificial Intelligence:
Probabilistic Reasoning and Decision Making

Prof. Lawrence Saul

Administrivia Syllabus Piazza GradeSource CAPEs


This course will introduce students to the probabilistic and statistical models at the heart of modern artificial intelligence. Specific topics to be covered include: probabilistic methods for reasoning and decision-making under uncertainty; inference and learning in Bayesian networks; prediction and planning in Markov decision processes; applications to intelligent systems, speech and natural language processing, information retrieval, and robotics.


This course is aimed very broadly at undergraduates in mathematics, science, and engineering. Prerequisites are elementary probability, linear algebra, and calculus, as well as basic programming ability in some high-level language such as C, Java, Matlab, R, or Python. (Programming assignments are completed in the language of the student's choice.) Students of all backgrounds are welcome.


The course will not closely follow a particular text. The following texts, though not required, may be useful as general references:


  • Lecturer: Lawrence Saul (saulcs.ucsd.edu)

  • Teaching assistants:
    Yuncong Chen (yuc007eng.ucsd.edu)
    Vineel Konduru (vkonduruucsd.edu)
    Soham Shah (srshaheng.ucsd.edu)
    Kritika Singh (kritikaeng.ucsd.edu)


  • Lectures: Tue/Thu 5:00-6:20 pm, WLH-2005.
  • Office hour: Fri 9-10 am, EBU3B-3214.
  • Discussion sections:
    • Mon 4:00 WLH 2207 (Kritika)
    • Mon 6:00 CSB 004 (Vineel)
    • Fri 11:00 PCYNH 121 (Yuncong)
    • Fri 4:00 WLH 2207 (Soham)
  • Tutoring hours:
    • Mon 11:00 B250A (Soham)
    • Wed 12:00 B240A (Kritika)
    • Fri 1:00 B250A (Vineel)
    • Fri 3:00 B275 (Yuncong)
  • Final exam: Thu Mar 20, 7-10 pm


  • homework (25%)
  • quizzes (40%)
  • final exam (35%)


Tue Jan 07Administrivia and course overview
Thu Jan 09Modeling uncertainty, review of probability.
Tue Jan 14Examples of probabilistic reasoning.HW 1 out.
Thu Jan 16Belief networks: from probabilities to graphs.
Tue Jan 21Conditional independence, d-separation.HW 1 due.
HW 2 out.
Thu Jan 23Inference in polytrees and loopy networks.
Tue Jan 28Learning, maximum likelihood estimation.HW 2 due.
HW 3 out.
Thu Jan 30Naive Bayes and Markov models.
Tue Feb 04Latent variable models, EM algorithm.HW 3 due.
Thu Feb 06Examples of EM algorithm.
Tue Feb 11Quiz #1.HW 4 out.
Thu Feb 13Hidden Markov models, speech recognition.
Tue Feb 18Viterbi and forward-backward algorithms.
Belief updating.
HW 4 due.
HW 5 out.
Thu Feb 20Reinforcement learning.
Tue Feb 25Markov decision processes.HW 5 due.
Thu Feb 27Policy evaluation, improvement, and iteration.
Tue Mar 04Quiz #2.
Thu Mar 06Bellman optimality equation, value iteration.HW 6 out.
Tue Mar 11Temporal difference learning, Q-learning.
Thu Mar 13Course wrap-up, odds and ends.HW 6 due.
Thu Mar 20Final exam