CSE 473, 22au: Introduction to AI

MWF at 2:30-3:20 in CSE2 G20.

Recordings in canvas.

Schedule

(subject to change)

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 at least 20 percent of office hours virtually. If you're still not able to make this time, please reach out to us on Ed.

If you have any additional feedback you would like to share, please use this anonymous feedback form.

We also have office hours in a number of different times and locations. All times are Pacific. Please use this queue.

• Luke Zettlemoyer, instructor, Friday 1:15-2:15, Allen 534
• Soham Gadgil, TA, Wednesday 11AM - 12PM, Gates 151
• Skyler Hallinan, TA, Monday 4PM - 5PM, Zoom
• William Howard-Snyder, TA, Wednesday 10:00-11:00 AM, Zoom
• Diya Joy, TA, Thursday 2:30-3:30PM, Zoom
• Yegor Kuznetsov, TA, Tuesday 1:30-3:00PM, Allen 4th Floor Breakout
• Logan Milandin, TA, Wednesday 5:00-6:00PM, Thursday 4:00-5:00PM Zoom
• Markus Andrej Schiffer, TA, Thursday 3:30 - 4:30, Gates 151
• Yunwei Zhao, TA, Friday 10:30 - 11:30, Zoom

Assignments

Homework (written)

Individual assignments graded on correctness and due by 10pm 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 the answers must be clearly visible (i.e., pencil may not work).
• Unless the question asks you to justify your answers, please do not add any explanations.
• When a question does ask you to justify your answer, it is enough to just provide justification for just the answer you chose.
• Please make sure to add the corresponding question tag to your solution to make grading easier. This may be cumbersome but will allow us to get you your homework grades more quickly.

Homeworks (HW)	Total Points	Due
1: Search	30	10/14/22
2: Advanced Search	30	10/28/22
3: Markov Decision Processes	30	11/10/22
4: RL and HMMs	30	11/22/22
5: Uncertainty	30	12/6/22

Projects (programming)

Individual assignments graded on correctness and due by 10pm on the day listed. Worth 50% of grade total.

Projects (PR)	Total Points	Due
0: Warm-up	N/A	Not graded
1: Search	25	10/19/22
2: Multi-Agent Search	25	11/03/22
3: Reinforcement Learning	25	11/17/22
4: Inference and Filtering	25	12/7/22 12/9/22

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.)

COVID

Please stay home if you're ill. Lectures are recorded and most office hours are held remotely. If one of the course staff becomes ill we will move the appropriate events online. Consult the UW policies for more information.

Grade

• Your grade is is divided equally between the written (50%) and programmaing (50%) assignments. I will also add up to 5% extra credit for students who actively participate in class or help others on the Ed discussion board.

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. These are subject to revision of types typographic, syntactic, and semantic. We will alert the class if any major changes are made.

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