CSE 473: Introduction to Artificial Intelligence
Autumn 2015
Basic Information
Course Staff:
Steve Tanimoto Course Instructor Office Hours: (no OH on Dec 11); Monday, Dec. 14, 2:00 PM-3:00 PM in CSE 624. |
Fereshteh Sadeghi Teaching Assistant Office Hours: Mon 10:30-11:20, in CSE 220. |
 Hayoun Oh Teaching Assistant Office Hours: Mon, Wed 12:30-1:20, in CSE 220. |
 Zac Iqbal Teaching Assistant Office Hours: Tues 10:30-12:20, in CSE 006 (ugrad lab). |
Calendar: The schedule of lectures and other course events is available in our calendar. You may wish to subscribe to it, in order to see course events listed in your own calendar.
Contact:
Our primary method of communication will be the Catalyst GoPost forum for this course.
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 SAV 264 (Savery Hall)
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, come to class on Day 1 (and note the code that will be given out there), and then fill out the online overload request form. This does not guarantee enrollment, but we will do our best.
Grading
Your grade will be 40% homework assignments, 20% midterm, 30% final exam, and 10% 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.
The bulk of the participation score will come from the worksheets we do in class. Each worksheet is worth up to 2 points, when an honest attempt at completing the activity is turned in by the end of the class in which it is given out. These worksheets can also be turned in up to two classes late, but for one point of participation credit. This system incentivizes contributions in-person to our class community, improving the learning environment. Catalyst Gradebook permits instructors to compute totals that automatically drop the two lowest-scoring items, and the plan is to exploit that capability when participation scores are figured.
Homework, including programming project files, should be turned in to this Catalyst DropBox.
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!
Feedback to the Course Staff
You may submit feedback (optionally anonymous) at any time on any aspect of the course here: Catalyst UMail feedback form
Homework Assignments
Project 0 due Oct. 7 @ 11:59 PM (lead TA: Zac)
Project 1: Search due Oct. 19 @ 11:59 PM (lead TA: Hayoun)
Project 2: Multi-Agent Search due Oct. 30 @ 11:59 PM (lead TA: Zac)
Project 3: Reinforcement Learning due Nov. 18 @ 11:59 PM (lead TA: Fereshteh)
Project 4: Ghostbusters due Dec. 2 @ 11:59 PM (lead TA: Fereshteh)
Competition (Optional) due Dec. 9 @ 11:59 PM (coordinating TA: Zac)
Project 5: Pattern Classification (questions 1, 2, 4, 5, and 6), due Dec. 11 @ 11:59 PM (lead TA: Hayoun)