CSEP 573: Applications of Artificial Intelligence

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Welcome to CSEP 573 (Winter 2010)

Mon 6:30-9:20 pm in EEB 045

Instructor: Rajesh Rao (rao at cs) Office: Allen Center 566 Office hours: By appointment

TA: Abe Friesen (afriesen at cs) Office hours: CSE 220, Mon 5:30-6:30 pm (and by appointment)

The goal of the course is to introduce students to classical as well as recently discovered methods in artificial intelligence, and explore their potential applications. The course will emphasize topics in search, data mining, and machine learning. Written and programming assignments will enable students to gain first-hand knowledge of the intricacies of techniques discussed in the lectures. The course will also include guest lectures by experts illustrating applications of AI in the field.

Syllabus & Schedule

Schedule:

Week	Dates	Topics & Lecture Notes	Readings	Homeworks
1	Jan 4	Introduction 2 slides/page 1 slide/page	Russell & Norvig, Chapters 1-2
2	Jan 11	Problem Solving using Search 2 slides/page 1 slide/page	Russell & Norvig, Chapters 3-5 (3,4,6 in 2nd ed)	HW #1 assigned
3	Jan 18	No class (University holiday)	Russell & Norvig, Chapters 7-8
4	Jan 25	Adversarial Search, Logic and Reasoning 2 slides/page 1 slide/page	Russell & Norvig, Chapters 8-9	HW #1 due
5	Feb 1	Logic, Uncertainty and Probabilistic Reasoning 2 slides/page 1 slide/page	Russell & Norvig, Chapters 13-14	HW #2 assigned
6	Feb 8	Invited lecture: Dieter Fox (Director, Intel Seattle Lab) Probabilistic Reasoning and Applications	Russell & Norvig, Chapters 14-15
7	Feb 15	No class (University holiday); Class on Feb 18 instead
7	Feb 18	Supervised Learning: Classification and Regression 2 slides/page 1 slide/page	Russell & Norvig, Chapter 18	HW #2 due; HW #3 assigned
8	Feb 22	Unsupervised Learning, Applications in Vision 2 slides/page 1 slide/page Guest Lecture: R. Chalodhorn (UW CSE): Applications in Robotics	Russell & Norvig, Chapters 20, 24, 25
9	Mar 1	Invited lecture: Joseph Sirosh (VP, Amazon, Inc.) Machine learning applications in industry	Talk Highlights and Useful Links: SnapTell Mobile Visual Search AdSense NetFlicks Competition Random Projections Random Forests CRM114 Email/Data Filtering Software	HW #3 due, HW #4 assigned
10	Mar 8	Overview: AI - Past, Present, and Future. 2 slides/page 1 slide/page Emerging areas: Guest Lecture by R. Scherer (UW CSE) on Brain-Computer Interfaces	Russell & Norvig, Chapters 22, 26, 27
11	Mar 15	Finals week (no final or class, work on HW #4)		HW #4 due Mar 16 midnight

Textbooks

• Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, Third Edition (2009) (required).
• Ian H. Witten & Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, McGraw-Hill, Second Edition (2005) (recommended).

Assignments

• Homework #1 (due Monday, Jan 25 before class time (6:30 pm))
• Homework #2 (due Thursday, Feb 18 in class)
• Homework #3 (due Monday, March 1 in class)
• Homework #4 (due Tuesday, March 16 before midnight)

Course Administration and Policies

• Subscribe to the course mailing list. (Please wait until first week of classes)
• Assignments should be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers (written and programming) must be your own work. You may consult the web for more informaion but answers or programs should not be copied from other websites. Any collaboration or material from the web must be fully acknowledged in your work. The PMP program expects students to maintain the utmost level of academic integrity in its courses.
• Assignments may be handed in up to a week late, at a penalty of 10% of the maximum grade per day.

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