CSE 473: Artificial Intelligence

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Welcome to CSE 473 (Autumn 2013) Rajesh Rao (Instructor) with Yi-Shu Wei (TA) and Hunter Whalen (TA) MWF 9:30-10:20 EEB 045 In this course, we will explore basic concepts and techniques in the field of Artificial Intelligence and come face to face with the challenges of building an "intelligent system." We will focus on methods for search, knowledge representation, logical reasoning, probabilistic reasoning, and machine learning. (Catalog Description. Prerequisite: CSE 326 or 332. Credits: 3). Announcements Wednesday, Sep 25: First day of class! Monday, Nov 11: No class - University holiday (Veterans day) Friday, Nov 29: No class - Thanksgiving holidays Sunday, Dec 8: FINAL EXAM is now available! Discussion Board, Dropbox, and Mailing List Feel free to discuss with your classmates topics from the lectures and homeworks on the CSE 473 Discussion Board. Homeworks can be turned in using the CSE 473 Dropbox. The mailing list for the class is: cse473a_au13@uw.edu. If you are enrolled in the class, you are already a member of the list. Staff Instructor: Rajesh Rao (rao at cs) Allen Center 566 Office Hours: By appointment (send email) TA: Yi-Shu Wei (yishuwei at uw.edu) Office Hours: Wednesdays 4:00-5:00pm in CSE 218 TA: Hunter Whalen (hwhalen at cs) Office Hours: Fridays 3:30-4:30pm in CSE 218 Textbook The (required) textbook is Russell & Norvig's "Artificial Intelligence: A Modern Approach" (aka AIMA), Prentice Hall, 3rd edition, 2009 (older editions do not contain material covered in class). You may also find the following free online textbook useful for more information on Markov decision processes (MDPs) and reinforcement learning (RL): Richard Sutton & Andrew Barto, Reinforcement Learning: An Introduction, MIT Press. Grading Your final grade will be assigned as follows: 50% projects 20% midterm 30% final Late Policy Project assignments should be submitted to the dropbox by the time indicated. Late assignments will result in a 10% deduction of points for each day that the assignment is late. Cheating Vs. Collaborating Guidelines Collaboration is a very good thing. On the other hand, cheating is considered a very serious offense! Concern about cheating creates an unpleasant environment for everyone. If you cheat, you risk losing your position as a student in the department and the college. The department's policy on cheating is to report any cases to the college cheating committee. What follows afterwards is not fun. So how do you draw the line between collaboration and cheating? Here's a reasonable set of ground rules. Failure to understand and follow these rules will constitute cheating, and will be dealt with as per University guidelines. The "Dancing with the Stars" Rule: This rule says that you are free to meet with fellow student(s) and discuss assignments with them. Writing on a board or shared piece of paper is acceptable during the meeting; however, you should not take any written (electronic or otherwise) record away from the meeting. After the meeting, engage in some mind-numbing activity (like watching an episode of "Dancing with the Stars"), before starting to work on the assignment. This will ensure that you are able to reconstruct what you learned from the meeting, by yourself, using your own brain. The Freedom of Information Rule: To ensure that all collaboration is on the level, you must always write the name(s) of your collaborators on your assignment.

	Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA  98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX [comments to Rajesh Rao]