Principal ideas and developments in artificial intelligence: Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, machine learning, natural language processing.
Course overlaps with: CSE 415; CSS 382; and TCSS 435. Prerequisite: CSE 312 and CSE 332.
Lecture for this course is Monday, Wednesday, and Friday, 2:30-3:20pm. Students are strongly encouraged to 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 tab of this webpage. Any necessary Zoom links will be available via Canvas.
Schedule updates, modifications, and details will be available via the schedule tab of this webpage.
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 tab of this webpage.
Assignments for this course are comprised of written homeworks (6) and programming projects (5). Homeworks and projects are intended to be completed independently. These assignments are graded on correctness. Most lectures will include practice problems which review the material and recieve 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 will be a written final exam for this course.
The final grade for this course will be (.5*HW_earned + .4*earned + .1*FINAL_earned + PP) / (100 + PP), such that completions of many practice problems will decrease the weights of the other assignment types.
More details about assignment specifcs and the grading policy may be found on the assignments tab of this webpage.
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 also will refrain from sharing problem sets and answers with students from other quarters. Additionally, unless otherwise specified, using generative AI or other on-line solutions is prohibited. Students who are found to have cheated on an assignment will receive an automatic zero for that assignment.
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.
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 (mh75 at uw.edu). In the event you are more comfortable with a different approach, please refer to the resources above, or use the anonymous feedback tool.