CSE 473, 22sp: Introduction to AI
MWF at 2:30 in SAV 260.
Recordings in canvas.
(subject to change)
Wk. 
Dates 
Lecture slides and content (optional) 
Reading (optional reading) 
Due 
1 
3/28, 30, 4/1 
Intelligence
;
Introduction
;
Agents
[Solutions]
;
Search
[Solutions]
Section Notes
[
Solutions
]

R&N, 1,2,3.1 
PR0 
2 
4/4, 6, 8 
Informed Search
[Solutions]
;
Adversarial Search
[Solutions]
Section Notes
[
Solutions
]

R&N 3.2end, 5.15.2; Search tool 

3 
4/11, 13, 15 4/14 
Adversarial Search
;
Efficient Adversarial Search
[Solutions]
;
(
Local Search
)
Section Notes
[
Solutions
]

R&N 5.35.5, (4) 
PR1 
4 
4/18, 20, 22 
Constraint Satisfaction Problems (CSPs)
[Solutions]
;
CSP Solvers
[Solutions]
Section Notes
[
Solutions
]

R&N 6.16.4, (6.5end); CSP demo 
PR2, HW1 
5 
4/25, 27, 29 
Markov Decision Processes (MDPs)
[Solutions]
;
MDP Solvers
[Solutions]
Section Notes
[
Solutions
]

R&N 17.117.3, (S&B 4.34.4) 
HW2 
6 
5/2, 4, 6 
Passive Reinforcement Learning (RL)
[Solutions]
;
Active RL
[Solutions]
Section Notes
Solutions

R&N 22.122.3, (S&B 5.15.5) 
HW3 
7 
5/9, 11, 13 
(
Probability
[Solutions]
)
;
Graphical Models
[Solutions]
;
Independence
[Solutions]
Section Notes
[
Solutions
]

R&N 12, 13.113.3 (MIT notes) 
PR3 
8 
5/16, 18, 20 
Exact Inference
[Solutions]
;
Bayes' Net Sampling
[Solutions]
;
(BN Construction)
Section Notes
[
Solutions
]

R&N 13.4end 
HW4 
9 
5/23, 25, 27 
Markov Models
[Solutions]
;
Dynamic Bayes' Nets and Particle Filters
[Solutions]
Section Notes
[
Solutions
]

R&N 14.1end; (S&B 14, 15) 
HW5 
10 
6/1, 3 
Fairness and Causality
;
Wrapup

Hardt's Note (B&H&N 1,2); R&N 27 
PR4, HW6 
For fastest response, contact us on Ed. Otherwise contact us over email at cse473staff@cs.uw.edu.
We try to keep asynchronous course communication brief (namely on this page and in our responses on Ed). Please don't interpret this as cold. If you have any questions please reach out directly, whether in class, office hours, or in a privately scheduled meeting.
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.
Those of us with a physical location listed will mainly hold our office hours there and generally not in a hybrid fashion.
We will be enforcing room limits in office hours so for those of you unable to fit we may use a queue.
All times are Pacific.
 Will, time: Mondays, 12:30  1:30 PM, location: Zoom.
 Instructor, Jared, time: Mondays, 3:30  4:30 PM, location: Allen 3rd Floor Breakout.
 Markus, time: Tuesdays, 4:30  5:30 PM, location: Zoom.
 Daniel, time: Tuesdays, 12:30pm  1:30pm, location: Allen Center 218.
 Phuong, time: Wednesdays, 3:30  4:30 PM, location: Zoom.
 Ku, time: Thursday, 2:30  3:30, location: Allen 4th Floor Breakout.
 William, time: Thursdays, 1:302:30 PM, location: Zoom.
 Yunwei, time: Fridays, 10:3011:30, location: Zoom.
In addition to these regular hours, we will offer one additional virtual hour on the due date of each assignment which we will post about on Ed and list next to the relevant assignment in the preceding tables.
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.
Homework (HW) 
Total Points 
Due 
TA 
OH when 
OH where 
1:
Search

40 
4/22 
Phuong 
3:30  4:30 PM 
Zoom 
2:
CSPs

30 
4/29 
Yunwei 
Friday, 4/29, 11:30  12:30 PM 
Zoom 
3:
MDPs

30 
5/6 
Markus 
Friday, 5/6, 11:30  12:30 PM 
Zoom and CSE2 151 
4:
QLearning

27 
5/18 
Yunwei 
Wednesday, 5/18, 13:30  14:30 PM 
Zoom 
5:
Uncertainty
[Solutions]

40 
5/27 
Phuong 
Friday, 5/27, 3:30  4:30 PM 
Zoom 
6:
HMMs

24 
6/3 
Will 
Friday, 6/3, 6:00  8:00 PM 
Zoom 
Individual assignments graded on correctness and due by 10pm on the day listed. Worth 50% of grade total.
Projects (PR) 
Total Points 
Due 
TA 
OH when 
OH where 
0:
Warmup

3 
4/1 
Phuong 
Friday, 4/1, 3:30  4:30 PM 
Zoom 
1:
Search

25 
4/11 
Daniel 
Monday, 4/11, 12:30pm  1:30pm 
Zoom 
2:
Multiagent

26 
4/20 
Daniel 
Wed, 4/20, 12:30pm  1:30pm 
Zoom 
3:
Qlearning

26 
5/13 
Daniel 
Friday, 5/13, 12:30pm  1:30pm 
Zoom 
4:
Inference and Filtering

32 
6/5 
Daniel 
Wed, 6/1, 12:30pm  1:30pm 
Zoom 
Optional, graded on completion, open for collaboration, and due at 10pm on the day one week after the corresponding lecture (no late days accepted). (Because we have 30 days of class and only 20 lectures we'll release the due dates as the lectures are completed.) Review the correct answers on gradescope or below after the submission date.
Each completed problem adds: (number of completed practice problems) * (10 / total number of practice problems) to your grade, which will then be renormalized. (E.g. completing all of them is worth 10% of grade, in which case written and programming assignments are worth 45% each.)
Links posted on Gradescope and solutions in the schedule.
Review
We offer semiweekly review sections to go over problemsolving techniques for the written homework. You can see the section handouts, solutions, times, and locations on the course calendar above. They will not be recorded.
 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 penaltyfree 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).
 If seventy percent of you complete the endofquarter evaluations everyone will get an additional late day.
 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.)
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.
 Your grade will be the proportion of points you achieve for the projects and the homework, each set weighted by one half.
 Additionally, completing practice problems will decrease the weight of the projects and homework, at a maximum of ten percent of the total.
 Practice problems are not extra credit. Your grade will be renormalized (set to the range of \([0, 1]\)) based on how many you complete.
 That is, their effect will be exponentially discounted as you approach a perfect score.
 There will be no curve. Rather, your grade will be rounded to the highest decimal point out of four.
 (The one possible exception to this is that no student gets a 4.0 in which case we would then increase every student's grade by the difference between the highest scoring student and 4.0. We do not expect this to happen but will alert you if there is an assignment for which the max score is greater than the maximum achieved score which would be a proxy for such an event.)
 If you're interested in specifics, we will use the following formula to calculate your grade:
 \( total_{hw} = \frac{1}{2 \times \sum_{i \in HW} max_{hw_i}} \sum_{i \in HW} (points_{hw_i} \times \frac{1}{5} late_{hw_i}), \)

\( total_{pr} = \frac{1}{2 \times \sum_{i \in PR} max_{pr_i}} \sum_{i \in PR} (points_{pr_i} \times \frac{1}{5} late_{pr_i}), \)

\( total_{pp} = \frac{\sum_{i=1}^{PP} pp_i}{10PP} \),
 \( grade = \bigg \lceil \frac{ total_{hw} + total_{pr} + total_{pp}}{1 + total_{pp}} \times 40 \bigg \rceil / 10.0 ,\)
 where the homework and project scores are \(points_{hw,pr} \in \mathbf{N}\), the maximum achievable scores are \(max_{hw,pr} \in \mathbf{N}\), each practice problem is \(pp \in \{0,1\}\), and each day late for an assignment \(late_{hw,pr} \in \{0, 1, 2, 3, 4\}\), counting contiguous days off (holidays and weekends) as one and late days only applied to the first (and all subsequent) assignments after your total late days number greater than six.
 Strongly Recommended: Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, PrenticeHall, 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:
 Potentially useful:
Please use Ed for course related questions.
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.
We welcome students from all backgrounds and adhere to the Allen Schoolâ€™s Inclusiveness Statement. If anything related to the course makes you feel unwelcome in any way, let the instructor know.
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 widelyvarying 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:
 Workschool balance
 Familial responsibilities
 Unexpected travel
 ... or anything else beyond your control which may negatively impact your performance in the class