CSE 473: Artificial Intelligence

CSE Home

About Us

Search

Contact Info

Home  Schedule  Assignments

Welcome to CSE 473 (Autumn 2012) Rajesh Rao (Instructor) with Jennifer Hanson (TA) and Evan Herbst (TA) MWF 9:30-10:20 MOR 230 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 Take-Home Final is here! Friday, Nov 23: No class - Thanksgiving holidays Monday, Nov 12: No class - University holiday (Veterans day) Sample midterm and solutions available! Practice questions available! Monday, Sep 24: First day of class! 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_au12@uw.edu. If you are enrolled in the class, you are already a member of the list. Staff Instructor: Rajesh Rao (rao at cs.washington.edu) Allen Center 566 Office Hours: By appointment (send email) TA: Jennifer Hanson (jlh87 at uw.edu) Office Hours: 11am-noon, Thursdays, Room 220 TA: Evan Herbst (eherbst at cs.washington.edu) Office Hours: 2:25-3:25pm, Tuesdays, Room 218 Textbook The (required) textbook is Russell & Norvig's "Artificial Intelligence: A Modern Approach," (Prentice Hall) 3rd edition, 2009. A copy of the book is on reserve in the Engineering library and can be checked out for 2-hour periods. 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 "American Idol" 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 "American Idol"), 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]