|CSE 150 - Winter 2013|
Introduction to Artificial Intelligence:
Probabilistic Reasoning and Decision Making
Prof. Lawrence Saul
This course will introduce students to the 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:
- Professor: Lawrence Saul (saulcs.ucsd.edu)
- Teaching assistants:
Matthew Der (mfdercs.ucsd.edu)
Matthew Elkherj (melkherjeng.ucsd.edu)
Phuc Nyguen (pxn002ucsd.edu)
- Lectures: Tue/Thu 2:00-3:20 pm, CSB-002.
- Office hour: Fri 1-2 pm, EBU3B-3214.
- Discussion sections: Fri 3-4 pm (WLH 2111), Mon 3-4 pm (Center 216).
- Tutoring hours: Mon 4-5 pm (EBU3B-B250a), Thu 3:30-4:30 pm (EBU3B-B250a), Fri 4-5 pm (EBU3B-B240a).
- Final exam: Thu Mar 21, 3-6 pm
- homework (25%)
- quizzes (40%)
- final exam (35%)
|Tue Jan 08||Administrivia and course overview||
|Thu Jan 10||Modeling uncertainty, review of probability.||
|Tue Jan 15||Examples of probabilistic reasoning.||HW 1 out.
|Thu Jan 17||Belief networks: from probabilities to graphs.||
|Tue Jan 22||Conditional independence, d-separation.||HW 1 due.|
HW 2 out.
|Thu Jan 22||Inference in polytrees and loopy networks.||
|Tue Jan 29||Learning, maximum likelihood estimation.||HW 2 due.|
HW 3 out.
|Thu Jan 31||Naive Bayes and Markov models.||
|Tue Feb 05||Latent variable models, EM algorithm.||HW 3 due.
|Thu Feb 07||Examples of EM algorithm.||
|Tue Feb 12||Quiz #1.||HW 4 out.
|Thu Feb 14||Hidden Markov models, speech recognition.||
|Tue Feb 19||Viterbi and forward-backward algorithms.|
|HW 4 due.|
HW 5 out.
|Thu Feb 21||Reinforcement learning.||
|Tue Feb 26||Markov decision processes.||HW 5 due.
|Thu Feb 28||Policy evaluation, improvement, and iteration.||
|Tue Mar 05||Quiz #2.||
|Thu Mar 07||Bellman optimality equation, value iteration.||HW 6 out.
|Tue Mar 12||Temporal difference learning, Q-learning.||
|Thu Mar 14||Course wrap-up, odds and ends.||HW 6 due.
|Thu Mar 21||Final exam||