|CSE 150 - Spring 2018|
Introduction to Artificial Intelligence:
Probabilistic Reasoning and Decision Making
Prof. Lawrence Saul
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:
- K. Korb and A. Nicholson, Bayesian Artificial Intelligence.
- S. Russell and P. Norvig,
Intelligence: A Modern Approach.
- R. Sutton and A. Barto,
- Lecturer: Lawrence Saul (saulcs.ucsd.edu)
- Teaching assistants:
Sparsh Gupta (spg005ucsd.edu)
Simran Kapur (sikapurucsd.edu)
Harsh Kumar (h1kumarucsd.edu)
Aishma Raghu (airaghuucsd.edu)
Nitesh Sekhar (sniteshucsd.edu)
Nemil Shah (nbshahucsd.edu)
- Lectures: Tue/Thu 3:30-4:50 pm, Center 109.
- Instructor office hour: Fri 10-11 am, EBU3B-3214.
- Discussion sections:
Mon 3-4 pm, HSS 2154 (Sparsh)
Mon 5-6 pm, HSS 2154 (Harsh)
Wed 11-noon, WLH 2206 (Nemil)
Wed 5-6 pm, WLH 2115 (Nitesh)
Fri 11-noon, SEQUO 147 (Simran)
Fri 1-2 pm, HSS 2154 (Aishma)
- TA office hours:
Mon 11:30-12:30 pm, CSE B250A (Aishma)
Tue 11-noon, CSE B250A (Sparsh)
Wed 1-2 pm, CSE B250A (Nitesh)
Thu 12-1 pm, CSE B260A (Nemil)
Thu 2-3 pm, CSE B250A (Harsh)
Fri 2:30-3:30 pm, CSE B250A (Simran)
- Final exam: Mon June 11, 3-6 pm
- homework (30%) - best 6 of 7
- midterm exam (20%)
- final exam (50%)
|Tue Apr 03||Administrivia and course overview.||
|Thu Apr 05||Modeling uncertainty, review of probability.|| |
|Tue Apr 10||Examples of probabilistic reasoning.||HW 1 out.|
|Thu Apr 12||Belief networks: from probabilities to graphs.||
|Tue Apr 17||Conditional independence, d-separation.||HW 1 due.|
HW 2 out.
|Thu Apr 19||Inference in polytrees and loopy networks.||
|Tue Apr 24||Learning, maximum likelihood estimation.||HW 2 due.|
HW 3 out.
|Thu Apr 26||Naive Bayes and Markov models.||
|Tue May 01||Latent variable models, EM algorithm.||HW 3 due.|
HW 4 out.
|Thu May 03||Examples of EM algorithm.||
|Tue May 08||Hidden Markov models, speech recognition.||HW 4 due.
|Thu May 10||Viterbi and forward-backward algorithms.|
|Tue May 15||Midterm exam
||HW 5 out.
|Thu May 17||Reinforcement learning.||
|Tue May 22||Markov decision processes.||HW 5 due.|
HW 6 out.
|Thu May 24||Policy evaluation, improvement, and iteration.||
|Tue May 29||Bellman optimality equation, value iteration.||HW 6 due.|
HW 7 out.
|Thu May 31||Temporal difference learning, Q-learning.||
|Tue Jun 05||TBA||
|Thu Jun 07||TBA||HW 7 due.
|Mon Jun 11||Final exam||