CSEP 573, 25au: Introduction to AI
Thursdays from 6:30-9:20 in CSE2 G20.
Recordings in canvas.
Schedule
(subject to change)
| Wk. | Dates | Lecture slides | Reading (optional reading) | Notes |
|---|---|---|---|---|
| 1 | 09/25 | Introduction; Agents; Search | R&N, 1,2,3.1 | Uninformed Search |
| 2 | 10/02 | Search (cont.); Informed Search | R&N 3.2-end, 5.1-5.2; Search tool | Informed Search |
| 3 | 10/09 | Adversarial Search; ExpectiMax | R&N 5.3-5.5, (4) | Adversarial Search |
| 4 | 10/16 | Markov Decision Processes (MDPs) | R&N 17.1-17.3, (S&B 4.3-4.4) | |
| 5 | 10/23 | Reinforcement Learning | R&N 22.1-22.5, (S&B 5.1-5.5) | |
| 6 | 10/30 | RL (cont.); Uncertainty | R&N 12, 13.1-13.3 MIT notes, R&N 15.1-15.3 | |
| 7 | 11/06 | Hidden Markov Models - Language models | R&N 15.1-15.3 | |
| 8 | 11/13 | HMMs Inference - Particle Filtering; Bayes Nets | R&N 14.1-14.3 | |
| 9 | 11/20 | Inference in Bayes Nets | R&N 14.4-14.5 | |
| 10 | 11/27 | (Thanksgiving) | ||
| 11 | 12/04 | Machine Learning + Applications | R&N 18.1-18.2; 20.1-20.2 |
Communication and Office Hours
The staff is available to help you in a number of different ways. Please consider asking any questions first on the Ed forum, so that others can also benefit from the shared responses. We will try to schedule office hours to accommodate students' schedules and will offer some office hours virtually. If you're still not able to make this time, please reach out to us on Ed.We also have office hours in a number of different times and locations.
- Hanna Hajishirzi, instructor, CSE 470
- Tianhua Tao, TA, office hour: Thursday 9:30-10:30pm, Google Meet.
- Hamish Ivison, TA, office hour: Wednesday 7:00-8:00pm, Zoom link.
- Jingwei Ma, TA, office hour: Tuesday 7:00-8:00pm, Zoom link.
- Mengyi Shan, TA, office hour: Monday 7:00-8:00pm, Zoom link. [Important: OH changed to 4:00-5:00pm on Sep 29]
Assignments
Programming Assignments
Individual assignments graded on correctness and due by 11:59pm on the day listed. Worth 50% of grade total.
| Programming Assignments (PA) | Total Points | Due |
|---|---|---|
| 0: Warm-up | N/A | Not graded |
| 1: Search | 25 | 10/16 |
| 2: Multi-Agent Search | 25 | 11/06 (TBD) |
| 3: Reinforcement Learning | 25 | 11/20 (TBD) |
| 4: Inference and Filtering | 25 | 12/04 (TBD) |
Written Assignments
Individual assignments graded on correctness and due by 11:59pm on the day listed. Worth 50% of grade total. Make sure your answers are selected and visible when you submit them. You may handwrite and scan the homework if you would like, but answers must be clearly visible (i.e., pencil may not work).
| Written Assignments (WA) | Points | Due |
|---|---|---|
| Midterm | 50 | 10/30 (TBD) | Final | 50 | 12/10 (TBD) |
Policies
Submitting
- All work will be turned in electronically.
- Assignments should be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course, pertinent to the Allen School's policy on academic misconduct.
- Each student has six penalty-free late days for the whole quarter. Consecutive days off (weekends or holidays) count as one late day. Other than that, any late submission will be penalized at 20 percent of the submitted grade per day (weekends count as one day). (This should incentive you to attempt the assignments even if you submit them quite late).
- The maximum late days that can be used per assignment is four.
- You must link pages to questions for written assignments submitted to gradescope. You will lose 0.25 points off the assignment if you do not do so. (For guidance watch this video on how to do this.)
Grade
- Your grade is is divided equally between the written (50%) and programmaing (50%) assignments.
Textbooks
- Strongly Recommended: Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, Fourth Edition (2020) [R&N].
- Given how fast the field of AI is moving, the third edition from 2010 will likely suffice, but will not be much of a resource for further investigation.
- Useful:
- Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans, Farrar, Straus, and Giroux. 2019.
- This is a popularly-oriented overview of AI which explains many of the intuitions and implications of the concepts we cover in this course.
- Richard Sutton & Andrew Barto, Reinforcement Learning: An Introduction Second Edition, MIT Press. 2018 (limited chapters freely available online) [S&B]
Discussion Board
Please use Ed for course related questions.
Lectures
Lecture slides will be posted on this site before the relevant day. We will alert the class if any major changes are made to correct errors, etc after posting.
Lecture videos should upload to canvas automatically.
Inclusion
We welcome students from all backgrounds and adhere to the Allen School’s Inclusiveness Statement. If anything related to the course makes you feel unwelcome in any way, let the instructor know.
Accommodation
We are eager to provide necessary accommodations.
For disability accommodations, please see the UW resources.
For religious accommodations, please see the UW resources.
We recognize that our students come from varied backgrounds and can have widely-varying circumstances. If you have any unforeseen or extenuating circumstance that arise during the course, please do not hesitate to contact the instructor to discuss your situation. The sooner we are made aware, the more easily these situations can be resolved. Extenuating circumstances may include:
- Work-school balance
- Familial responsibilities
- Unexpected travel
- ... or anything else beyond your control which may negatively impact your performance in the class