Introduction to Artificial Intelligence

The course covers the 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.

General Information

Schedule and Meeting Policies

Students are strongly encouraged to attend lectures 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 Teaching Team tab and/or the schedule tab of this webpage. Any necessary Zoom links will be available via the Teaching Team tab or ED.

Schedule updates, modifications, and details will be available via the schedule tab of this webpage.


 

Textbooks

The recommended, but not required, text book for this course is Artificial Intelligence: A Modern Approach, by Stuart Russell & Peter Norvig, Prentice-Hall, Fourth Edition (2020) [R&N].

There are a number of additional recommended texts, as well as other resources, available under the resources tab of this webpage.

Assignments and Grading Policies

Assignments for this course are comprised of written homeworks (4), programming projects (4), and worksheets (approximately 10). Homeworks and projects are weighted equally for the final grade, and are intended to be completed independently. These assignments are graded on correctness. Some lectures sessions will include worksheets which reinforce the material and receive credit upon submission.

There are no midterm or final exams in this course, there will be two 30-minute quizzes intended to help reinforce your learning of the material and that will count 5 percent each to the course grade.

More details about assignment specifcs and the grading policy may be found on the assignments tab of this webpage.

Academic Integrity

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, and following assignment guidelines on group work. The use of generative AI in the programming projects and sets of written exercises in CSE 473 is not permitted unless expressly permitted in writing for the particular assignment. Students who are found to have cheated on an assignment will receive an automatic zero for that assignment.

Accommodations and Support

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 (tanimoto 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.