Recordings in canvas.
(subject to change)
|Wk.||Dates||Lecture slides (optional slides)||Reading (optional reading)||Due|
|1||1/3, 5, 7||Intelligence ; Introduction ; Agents ; Search||R&N, 1,2,3.1||PR0|
|2||1/10, 12, 14||Informed Search||R&N 3.2-end, 5.1-5.2; Search tool|
|3||1/19, 21||Adversarial Search ; Efficient Adversarial Search||R&N 5.3-5.5, (4)||PR1|
|4||1/24, 26, 28||Constraint Satisfaction Problems (CSPs) ; CSP Solvers||R&N 6.1-6.4, (6.5-end); CSP demo||HW1|
|5||1/31, 2/2, 4||Markov Decision Processes (MDPs) ; MDP Solvers||R&N 17.1-17.3, (S&B 4.3-4.4)||PR2,HW2|
|6||2/7, 9, 11||Passive Reinforcement Learning (RL) ; Active RL||R&N 22.1-22.3, (S&B 5.1-5.5)||HW3|
|7||2/14, 16, 18||Probability ; Graphical Models||R&N 12, 13.1-13.3||PR3|
|8||2/23, 25||Graphical Models ; Independence||R&N 13.4-end||HW4|
|9||2/28, 3/2, 4||Exact Inference ; Markov Models||R&N 14.1-end; (S&B 14, 15)||HW5|
|10||3/7, 9, 11||Bayes' Net Sampling , Dynamic Bayes' Nets and Particle Filters ; Fairness and Causality ; Wrap-up||Hardt's Note (B&H&N 1,2); R&N 27||HW6|
For fastest response, contact us on Ed. Otherwise contact us over email at firstname.lastname@example.org.
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.
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.
|Homework (HW)||Total Points||Due||Hours spent?||TA||OH when||OH where|
|4: Q-Learning||26||2/25||feedback||Jeffrey||3:30-4:30pm||CSE 153 or online|
|5: Uncertainty||26||3/4||feedback||Jeffrey||3:30-4:30pm||CSE 153 or online|
Individual assignments graded on correctness and due by 10pm on the day listed. Worth 50% of grade total.
|Projects (PR)||Total Points||Due||Hours spent?||TA||OH when||OH where|
Optional, graded on completion, open for collaboration, and due at 10pm on the day of the subsequent 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.
|Lecture 02: Agents [Solutions]|
|Lecture 03: Search [Solutions]|
|Lecture 04: Informed Search [Solutions]|
|Lecture 05: Adversarial Search [Solutions]|
|Lecture 06: Expected Search [Solutions]|
|Lecture 07: Constraint Satisfaction Problems (CSPs) [Solutions]|
|Lecture 08: CSP Solvers [Solutions]|
|Lecture 09: Markov Decision Processes (MDPs) [Solutions]|
|Lecture 10: MDP Solvers [Solutions]|
|Lecture 11: Passive Reinforcement Learning (RL) [Solutions]|
|Lecture 12: Active RL [Solutions]|
|Lecture 13: Uncertainty [Solutions]|
|Lecture 14: Graphical Models [Solutions]|
|Lecture 15: Bayes' Nets [Solutions]|
|Lecture 16: d-separation [Solutions]|
|Lecture 17: Markov Models [Solutions]|
|Lecture 17.2: Markov Models [Solutions]|
|Lecture 18: Sampling|
|Lecture 18.2: Particle Filtering|
|Lecture 19: Fairness and Causality|
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.
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 School’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.