|CSE 150 - Spring 2017|
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:
Saicharan Duppati (sduppatieng.ucsd.edu)
Shubham Gupta (shg061eng.ucsd.edu)
Nivetha Thiruverahan (nthiruveeng.ucsd.edu)
Zhen Zhai (zzhaieng.ucsd.edu)
Leon Cheung (ljcheungucsd.edu)
Sara Farsi (safarsieng.ucsd.edu)
Zhiwei Jia (zjiaucsd.edu)
Justin Lizama (jnlizamaucsd.edu)
- Lectures: Tue/Thu 8:00-9:20 pm, Center 101.
- Instructor office hour: Fri 10-11 am, EBU3B-3214.
- Discussion sections:
Mon 4-5 pm, Center 115 (Zhen)
Mon 7-8 pm, Pepper Canyon Hall 106 (Sai)
Thu 7-8 pm, Center 115 (Shubham)
Fri 12-1 pm, CSB 001 (Nivetha)
- TA office hours:
Mon 10-11 am, EBU3B-4217 (Zhen)
Mon 3-4 pm, EBU3B-B250a (Nivetha)
Thu 12:30-1:30 pm, EBU3B-4217 (Shubham)
Fri 3-4 pm, EBU3B-B260a (Saicharan)
- Tutoring hours:
Mon 10 am-12 pm, dungeon (Leon)
Tue 10-11 am, TBA (Sara)
Wed 3:30-5:30 pm, TBA (Justin)
Thu 9:30 am-12:30 pm, EBU3B-B250a (Zhiwei)
Fri 2-3 pm, dungeon (Leon)
Sat 10 am-12 pm, TBA (Sara)
- Final exam: Thu June 15, 8-11 am
- homework (30%) - best 6 of 7
- midterm exam (20%)
- final exam (50%)
|Tue Apr 04||Administrivia and course overview.|| |
|Thu Apr 06||Modeling uncertainty, review of probability.|| |
|Tue Apr 11||Examples of probabilistic reasoning.||HW 1 out.
|Thu Apr 13||Belief networks: from probabilities to graphs.||
|Tue Apr 18||Conditional independence, d-separation.||HW 1 due.|
HW 2 out.
|Thu Apr 20||Inference in polytrees and loopy networks.||
|Tue Apr 25||Learning, maximum likelihood estimation.||HW 2 due.|
HW 3 out.
|Thu Apr 27||Naive Bayes and Markov models.||
|Tue May 02||Latent variable models, EM algorithm.||HW 3 due.|
HW 4 out.
|Thu May 04||Examples of EM algorithm.||
|Tue May 09||Hidden Markov models, speech recognition.||HW 4 due.
|Thu May 11||Viterbi and forward-backward algorithms.|
|Tue May 16||Midterm exam
||HW 5 out.
|Thu May 18||Reinforcement learning.||
|Tue May 23||Markov decision processes.||HW 5 due.|
HW 6 out.
|Thu May 25||Policy evaluation, improvement, and iteration.||
|Tue May 30||Bellman optimality equation, value iteration.||HW 6 due.|
HW 7 out.
|Thu Jun 01||Temporal difference learning, Q-learning.||
|Tue Jun 06||TBA.||HW 7 due.
|Thu Jun 08||TBA.||
|Thu Jun 15||Final exam||