AUTUMN 2017 / CSE 473: Introduction to Artificial Intelligence

Course Instructor

Dieter Fox
Office Hours:
3:30 - 4:20PM Wed, CSE 586

Teaching Assistants

Nicholas Ruhland
Office Hours:
10:30 - 11:30AM Fri, CSE 021

Kenny Le
Office Hours:
3:30-4:30PM Thurs, CSE 021 TBA

Vardhman Mehta
Office Hours:
4:30 - 5:30PM Fri, CSE 007
Joseph Zhong
Office Hours:
3:30 - 4:20PM Mon, CSE 007



Our primary method of communication will be the Piazza site for this course:

If you truly wish to use old-fashioned email, you may email all instructors at cse473-instr [at]


Class Times & Locations:

Monday, Wednesday, Friday 2:30-3:20PM in AND 223


Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Third Edition (2009)



The programming projects in this course are based on those from This link is provided for reference only as our projects may differ.

AIMA Open Source API

The course and textbook feature an open-source "AI API", giving an overview of the algorithms and pseudocode used in the textbook. As an publicly and actively maintained open-source resource, you are encouraged to explore it and follow along as we cover material from the textbook and in lecture.

Grading and Late Policy:

Your grade will be 45% programming assignments, 20% midterm, 35% final exam, class participation.

Each student has two penalty-free late days for the whole quarter. All other late submissions will be penalized 10% of the maximum grade per day.

Assignments will 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.

Academic Honesty

It is encouraged that you discuss your ideas with each other and consult online sources to better understand the material. However, your code must be written entirely by yourself. As a rule, you should never look at or run anyone else's code for the assignment, whether the code was written by someone currently in the class, or someone who took it previously, even at another university. Reading pseudocode for generic algorithms (like alpha-beta pruning or A* search) is perfectly OK. If you use a source very closely, for example, converting a pseudocode implementation of A* to python, academic integrity demands that you cite the source (in a comment). You will not be penalized for this; on the contrary, the citation may help us to understand why your implementation is so similar to someone else's, in case they use and cite the same source. We do compare everyone's projects to each other and to past submissions to detect logical redundancy. When two assignments are too similar to have occurred by chance, we have to look into whether something improper occurred. These investigations are not fun for anyone involved. If you have questions, please ask!

Overflow Add Request

If you are not yet enrolled in the course, and wish to be, please follow instructions on the following link:

Anonymous Feedback

You may submit anonymous feedback at any time on any aspect of the course here

Homework Assignments


Midterm (Nov. 3 Fri)

Midterm will be held on Nov. 3 (Fri) during lecture time. Closed book, no calculators, no cheatsheet.

Midterm scores posted: Solutions

Practice Midterm Materials:

Final Exam (12 December)

Final will be held on 2:30-4:20PM 12 December (Tuesday of finals week) at AND 223. Closed book, no calculators. The final will be cumulative with an emphasis on the post-midterm material.

The cheatsheet provided on the exam is posted here:

The exam has been posted here, and the solutions have been posted here.

Practice Final Materials: