Principal ideas and developments in artificial intelligence: Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, machine learning, natural language processing. Not open for credit to students who have completed CSE 415. Prerequisite: CSE 312, CSE 332.
Staff:
Professor: Dr. Taylor Kessler Faulkner (taylorkf@cs.washington.edu)
TAs:
Shreya Sathyanarayanan
Rongwu Danny Xu
Jasmine Chi
Emilia Gan
Ankhita Sathanur
William Ong
Kiran Kaur
Time and Location: MWF 2:30-3:20 PM, Bill & Melinda Gates Center For Computer Science & Engineering (CSE2) G20
Office hours: On Canvas Schedule Page
Canvas: Used for gradebook and syllabus
Ed Discussion: Used for announcements, questions, course discussion, reaching the staff
Gradescope: Used for submitting coding and written assignments.
Lecture for this course is Monday, Wednesday, and Friday, 2:30-3:20pm. Students are strongly encouraged to attend in person; classes will have interactive learning activities that you will miss out on if you do not attend in person. However, lectures will be recorded and available via Panopto on Canvas. Students who are feeling unwell as asked to stay home and review the recorded material.
Office hours will be frequent and posted via the schedule page on Canvas.
Schedule updates, modifications, and details will be available via the schedule page on Canvas.
The course staff will keep an eye on the Ed board for questions M-F 9AM-5PM.
The strongly recommended text book for this course is Artificial Intelligence: A Modern Approach, by Stuart Russell & Peter Norvig, Prentice-Hall, Fourth Edition (2020) [R&N]. Regular reading assignments from this text will be recommended.
There are a number of additional recommended texts, as well as other resources, available under the resources page on Canvas.
Assignments for this course are comprised of written homeworks (5) and programming projects (4). Homeworks and projects are weighted equally for the final grade, and are intended to be completed independently. These assignments are graded on correctness. Most lectures will include practice problems which review the material and receive credit upon submission. In addition to this work there will be assigned reading, which is not evaluated but will enhance student mastery of the subject.
There are no traditional exams offered in this course.
More details about assignment specifics and the grading policy may be found on the assignments page on Canvas.
Each of the Homeworks and Projects may be handed in up to 2 days late penalty-free. After that there is a 20% deduction for every additional day. This means that Homeworks and Projects may be handed in up to 6 days late for some partial credit. Students will benefit from handing in homework on time; previous experience shows that students who rely heavily on using late days struggle more as the course goes on, however, this late policy should provide flexibility for typical life disruptions. Pay attention to the due time as well as the due date. There is a small grace period for last-minute online submission issues, but you should plan ahead to avoid depending on it.
Practice problems are due by the beginning of the subsequent lecture, and no late submissions are permitted. In other words, the practice problem for lecture 2 (on Wednesday) is due Friday afternoon, and the practice problem for lecture 3 (on Friday) is due Monday afternoon.
Each Homework and Project will be graded based on correctness, and awarded a number of points. More challenging assignments will have a high maximum point value. Autograders will evaluate correctness (although manual review is possible). A late penalty will be applied to the final score if necessary L = .2 * (days_late - 2). The total subscore for Homeworks (HW_earned) and Projects (PR_earned) will be the percent possible points earned in that category.
Practice problems will be assigned a grade of 0 (not complete) or 1 (complete). No late submissions are possible, so no penalty is applied. The total subscore will be PP = 10 * #complete / #possible
The final percentage grade for the course will be (.5*HW_earned + .5*PR_earned + PP) / (100 + PP), such that completion of many practice problems will decrease the weights of the Homeworks and Projects. This percentage is converted to a 4.0 score using a linear transformation.
Most assignments for this course will be submitted to Gradescope. Please verify that you can access and interact with Gradescope now. Most assignments have autograders which will verify submission and provide some feedback about performance. Assignments may be submitted more than once, up until the due date.
For Homework assignments students are asked to tag their assignments. If assignments are not tagged, a penalty of .25 points will be taken. For more information on this process, see this video. Written assignments may be submitted as edited documents, or hand-written and scanned documents.
This course follows University and CSE guidelines for academic integrity. Any attempt to misrepresent the work you submit will be dealt with via the appropriate University mechanisms, and your instructor will make every attempt to ensure the harshest allowable penalty. The guidelines for this course and more information about academic integrity are in a separate document (CSE misconduct). You are responsible for knowing the information in that document. Please notice that you should not, in any situation, borrow another person's code or provide yours to a fellow student, including students in other quarters of this course. You may discuss assignments at a high level and study together, but you may not not show your code to other students in any capacity (including through public posts on Ed). You also will refrain from sharing problem sets and answers with students from other quarters, and following assignment guidelines on group work. Students should be able to answer oral questions about their work at any time. If you find yourself in doubt about your solution, please ask staff for help on the topic. Students who are found to have cheated on an assignment will receive an automatic zero for that assignment.
Tip: A good rule of thumb to ensuring any collaboration with other students (outside of assigned group work) is allowed is to not take written notes, photographs, or other records during your discussion and wait at least 30 minutes after completing the discussion before returning to your own work. For most students, this will result in you only bringing the high-level concepts of the collaboration back to your work, and ensuring that you reconstruct the ideas on your own.
In this course, students are permitted to use AI-based tools (such as UW’s version of Copilot) on certain questions in assignments in which they are specifically mentioned, in writing, as allowed. All sources, including AI tools, must be properly cited. Use of AI in ways that are inconsistent with the parameters above will be considered academic misconduct and subject to investigation.
Please note that AI results can be biased and inaccurate. It is your responsibility to ensure that the information you use from AI is accurate. Additionally, pay attention to the privacy of your data. Many AI tools will incorporate and use any content you share, so be careful not to unintentionally share copyrighted materials, original work, or personal information.
Learning how to thoughtfully and strategically use AI-based tools may help you develop your skills, refine your work, and prepare you for your future career. If you have any questions about citation or about what constitutes academic integrity in this course or at the University of Washington, please feel free to contact me to discuss your concerns.
This course adheres to University standards including those guidelines laid out about Academic Integrity and Student Conduct. We refer students to support and accommodation services including Disability Services, Religious Accommodations, and Safe Campus resources. More details on Disability Services and Religious Accommodations are also included below.
This instructor seeks to ensure all students are fully included in each course, and strives to create an environment that reflects community and mutual caring. I encourage students with concerns about classroom or course climate to contact me directly (taylorkf@cs.washington.edu). In the event you are more comfortable with a different approach, please refer to the resources above, or use the anonymous feedback tool.
Your experience in this class should not be affected by any disabilities that you may have. The Disability Resources for Students. The DRS office can help you establish accommodations with the course staff.
DRS instructions for students:
Your experience in this class is important to me. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law. If you have already established accommodations with Disability Resources for Students (DRS), please activate your accommodations via myDRS so we can discuss how they will be implemented in this course.
If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), contact DRS directly to set up an Access Plan. DRS facilitates the interactive process that establishes reasonable accommodations. Contact DRS at disability.uw.edu.
Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form. (https://registrar.washington.edu/students/religious-accommodationsrequest/).