| CSE 150 - Summer 2017 Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision Making
Prof. Lawrence Saul |
| Administrivia | Syllabus | Piazza | GradeSource | CAPEs |
Subject
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.
Prerequisites
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.
Texts
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, Artificial Intelligence: A Modern Approach.
- R. Sutton and A. Barto, Reinforcement Learning: An Introduction.
Instructors
- Lecturer: Lawrence Saul (saul
cs.ucsd.edu)
- Teaching assistants:
Sai Spoorthy Padigi (spadigieng.ucsd.edu)
Vikas Sakaray (vsakarayeng.ucsd.edu)
Saksham Sharma (sas111eng.ucsd.edu)
Meetings
- Lectures: Tue/Thu 11:00a-1:50p, Center 212.
- Discussion sections:
Mon 12-1p, CSE 2154 (Sai)
Wed 9:30-10:30a, Center 212 (Vikas)
Fri 12-1p, CSE 2154 (Saksham)
- Office hours:
Mon/Fri 10-11a, CSE 3214 (Prof. Saul)
Mon 2-3p, Tue 3-4p, CSE B260A (Vikas)
Wed 2-3p, Thu 3-4p, CSE B260A (Saksham)
Wed 11-noon, Fri 3-4p, room TBA (Sai) - Final exam: Sat Aug 5, 11:30a-2:30p
Grading
- homework (50%)
- final exam (50%)
Syllabus
| Thu July 05 | Administrivia and course overview. Modeling of uncertainty, review of probability. | |
| Tue July 10 | Examples of probabilistic reasoning. Belief networks: from probabilities to graphs. | HW 2 |
| Thu July 12 | Conditional independence, d-separation. Inference in polytrees and loopy networks. | HW 3 |
| Tue July 17 | Learning, maximum likelihood estimation. Naive Bayes and Markov models. | HW 4 |
| Thu July 19 | Latent variable models, EM algorithm. Examples: noisy-OR, clustering. | HW 5 |
| Tue July 24 | Hidden Markov models, automatic speech recognition. Inference and learning in HMMs. | HW 6 |
| Thu July 26 | Introduction to reinforcement learning. Markov decision processes. | HW 7 |
| Tue Aug 01 | Policy evaluation, greedy policies, policy improvement. Policy and value iteration. | HW 8 |
| Thu Aug 03 | Temporal difference learning, Q-learning. Course wrap-up and review. | |
| Sat Aug 05 | Final exam |