CSE 473: Introduction to Artificial Intelligence

SPRING 2015

Basic Information

Course Staff:


Dieter Fox
Course Instructor
Office Hours:
Fri 11:00-12:00, CSE 586
Peter Henry
Teaching Assistant
Office Hours:
Wed, Thu 1:00-2:00, CSE 220
Nao Hiranuma
Teaching Assistant
Office Hours:
Tue 1:00-2:00, Fri 12:00-1:00 CSE 021

Contact:

Our primary method of communication will be the Piazza site for this course: https://piazza.com/washington/spring2015/cse473/.

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

Class times & locations: Monday, Wednesday, Friday, 9:30-10:20am in RAI 121 MUE 153

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

Pac-Man: The programming projects in this course are based on those from http://ai.berkeley.edu/project_overview.html. This link is provided for reference only as our projects may differ.

Overflow Add Request

If you are not yet enrolled in the course, and wish to be, please send the following information to cse473-instr [at] cs.washington.edu

This does not guarantee enrollment, but we will do our best.

Grading

Your grade will be 50% homework assignments, 15% midterm, 30% final exam, and 5% class participation.

Assignments may be handed in up to five days late, at a penalty of 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!

Anonymous Feedback

You may submit anonymous feedback at any time on any aspect of the course here: https://catalyst.uw.edu/webq/survey/djpeter/265869

Homework Assignments

Project 0

Project 1: Search due 4/20 @ 9:30 am

Project 2: Multi-Agent Search due 5/1 @ 9:30 am

Project 3: Reinforcement Learning due 5/18 @ 9:30 am

Project 4: Ghostbusters due 5/29 @ 9:30 am

Homework 5: Bayes Nets due 6/5 @ 9:30 am