CSE 250A. Principles of Artificial Intelligence:
Probabilistic Reasoning and Decision-Making
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| Administrivia | Syllabus | Piazza | GradeSource |
Subject
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.
Prerequisites
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 150 covers largely the same material as CSE 250A, but at a slower pace and less advanced mathematical level. The homework assignments (and exams) in CSE 250A are longer and more challenging.
Administrivia
- Professor: Lawrence Saul
- Teaching assistants: Sheeraz Ahmad, Long Jin, and Huaipeng Zhang.
- Lectures: Tue/Thu 12:30 am - 1:50 pm, HSS 1330
- Sections: TBA
- Instructor office hour: Fri 9-10 am @ CSE 3214.
- TA discussions:
Fri 2-3 pm @ PCYNH 120 (Sheeraz)
Mon 2-3 pm @ PCYNH 120 (Long)
Mon 7-8 pm @ HSS 1330 (Zhang)
- Grading: homework (25%), two quizzes (40%), final exam (35%).
Textbooks
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:- Artificial Intelligence: A Modern Approach (Russell & Norvig, 2010)
- Machine Learning: A Probabilistic Perspective (Murphy, 2012)
- Reinforcement Learning: An Introduction (Sutton & Barto, 1998)
- Pattern Recognition and Machine Learning (Bishop, 2006)
Syllabus
| Thu Oct 02 | Administrivia and course overview. | |
| Tue Oct 07 | Modeling uncertainty, review of probability, explaining away. | HW 1 out. |
| Thu Oct 09 | Belief networks: from probabilities to graphs. | |
| Tue Oct 14 | Conditional independence, d-separation, polytrees. | HW 1 due. HW 2 out. |
| Thu Oct 16 | Algorithms for exact and approximate inference. | |
| Tue Oct 21 | Maximum likelihood estimation; Markov models of language; naive Bayes models of text. | HW 2 due. HW 3 out. |
| Thu Oct 23 | Linear and logistic regression. Numerical optimization. | |
| Tue Oct 28 | Latent variable modeling. Expectation-Maximization (EM) algorithm. Auxiliary functions. | HW 3 due. |
| Thu Oct 30 | EM algorithm: derivation, proof of convergence. | |
| Tue Nov 04 | Quiz #1 | HW 4 out. |
| Thu Nov 06 | Examples of EM; applications to language modeling. | |
| Tue Nov 11 | Veteran's Day | HW 5 out. |
| Thu Nov 13 | Hidden Markov models, automatic speech recognition, Viterbi algorithm. | HW 4 due. |
| Tue Nov 18 | Forward-backward algorithm, Gaussian mixture models, Kalman filters. | HW 5 due. HW 6 out. |
| Thu Nov 20 | Reinforcement learning (RL), Markov decision processes. | |
| Tue Nov 25 | Policy evaluation, policy improvement. | HW 6 due. |
| Thu Nov 27 | Thanksgiving Holiday | |
| Tue Dec 02 | Quiz #2 | HW 7 out. |
| Thu Dec 04 | Policy iteration, value iteration. | |
| Tue Dec 09 | Stochastic approximation theory, temporal difference prediction. | |
| Thu Dec 11 | Q-learning, extensions of RL. | HW 7 due. |
| Fri Dec 19 | Final exam. |