This course will provide an overview of concepts and techniques in the area of classical Artificial Intelligence. See class schedule below.
Lectures: Tuesday & Thursday 10:00-11:15 in South College W245
Credit: 3 units
Class materials will be posted to the Moodle course.
Discussions will happen on the Google group or over Moodle.
| Weekday | Date | Title | References. Textbook reading is mandatory, the papers are optional. |
|---|---|---|---|
| Tuesday | 1/22/2019 | Introduction | AIMA Ch. 1 & 2; Horvitz et al. "Decision theory in expert systems and artificial intelligence" Hassabis et al. "Neuroscience-Inspired Artificial Intelligence" Gharamani "Probabilistic machine learning and artificial intelligence" Scholkopf "Learning to see and act" |
| Thursday | 1/24/2019 | Problem Solving as Search. Heuristics. | AIMA Ch. 3 |
| Tuesday | 1/29/2019 | Abstraction and Hierarchical Search | - |
| Thursday | 1/31/2019 | Local Search | AIMA Ch. 4 |
| Tuesday | 2/5/2019 | Resource-bounded Search Techniques | - |
| Thursday | 2/7/2019 | Adversarial Search | AIMA Ch. 5 |
| Tuesday | 2/12/2019 | Constraint Satisfaction | - |
| Thursday | 2/14/2019 | Constraint Satisfaction (continued) | AIMA Ch. 6 |
| Tuesday | 2/19/19 | No class | [Monday schedule] |
| Thursday | 2/21/2019 | Principles of Knowledge Representation and Reasoning | |
| Tuesday | 2/26/2019 | Inference in Propositional Logic | AIMA Ch. 7 |
| Thursday | 2/28/2019 | Inference in First Order Logic | AIMA Ch. 8 & 9 |
| Tuesday | 3/5/2019 | Probabilistic Reasoning using Bayesian Networks | - |
| Thursday | 3/7/2019 | Exact Inference in Belief Networks. | AIMA Ch. 13 |
| Tuesday | 3/12/2019 | No class | [Spring break] |
| Thursday | 3/14/2019 | No class | [Spring break] |
| Tuesday | 3/19/2019 | Approximate Inference in Belief Networks. [Guest Lecture] | AIMA Ch. 14 |
| Thursday | 3/21/2019 | Project pitches or midterm. | [May be swapped with the 19th.] |
| Tuesday | 3/26/2019 | Decision Theory | - |
| Thursday | 3/28/2019 | Decision Networks | AIMA Ch. 16 |
| Tuesday | 4/2/2019 | Planning | AIMA Ch. 10 |
| Thursday | 4/4/2019 | Planning under Uncertainty | AIMA Ch. 11 |
| Tuesday | 4/9/2019 | POMDPs | TBA |
| Thursday | 4/11/2019 | Reinforcement Learning | AIMA Ch. 21 |
| Tuesday | 4/16/2019 | Deep Reinforcement Learning | TBA |
| Thursday | 4/18/2019 | Multi-agent Systems | TBA |
| Tuesday | 4/23/2019 | Advanced Topic I | TBA |
| Thursday | 4/25/2019 | Advanced Topic II | TBA |
| Tuesday | 4/30/2019 | Recap | Sample exam questions |
Exam exception policy: If you have any special needs/circumstances pertaining to an exam, you must talk to the instructor before the exam.
Late homework policy: If you cannot turn in a homework on time, you will need to discuss with the instructor at least one day in advance.
Regrade policy: Any requests for regrading must be submitted within a week of receiving the grade and preferably discussed during office hours. Each TA will be responsible for a different part of the homework, as indicated when the assignment is issued, so please direct questions appropriately. Only contact the instructor after discussing the issue with the TAs.
Copyright/distribution notice: Many of the materials created for this course are the intellectual property of the course creator, Shlomo Zilberstein and the course instructor, Ina Fiterau. This includes, but is not limited to, the syllabus, lectures and course notes. Except to the extent not protected by copyright law, any use, distribution or sale of such materials requires the permission of the instructor. Please be aware that it is a violation of university policy to reproduce, for distribution or sale, class lectures or class notes, unless copyright has been explicitly waived by the faculty member.