Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
CSE 573 - Introduction to Artificial Intelligence - Winter 2016
Tues, Thurs 12:00-1:20pm in MGH 234 (moved to MGH 251 on feb 9 & 11)
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Instructor: Dan Weld (weld at cs dot washington dot edu)
Office hours: Fri 10am in CSE 588 or by email
TA: Chris Lin (chrislin at cs dot washington dot edu)
Office hours: Thu 1:30-2:30pm or Mon 2-3pm in CSE 382, or by email.
Cancelled 2/12-2/17 (Chris OOT).

Final Exam

Is here: (exam); it's due Friday at 10:30am. I prefer a hardcopy of the exam for ease of grading (deliver to CSE main office or under my door), but if that is a hardship you may email it to me and Chris. Here are the solutions.


Date Topics & Lecture Notes Readings
January 5 Introduction, Agents (slides) R&N Ch. 2; (Optional Ch 1)
January 7 Problem Spaces, Search (through A*) (slides) R&N Ch. 3; try this cool interactive search visualization
January 12 Heuristic generation, pattern DBs, local search (slides) R&N Ch. 4
January 14 Constraint Satisfaction I - formulation, backtracking, FC, AC, heuristics (slides) R&N Ch. 6, sections 6.1-6.3
January 19 Constraint Satisfaction II - structure, local search (slides); SAT Solving (slides) R&N sections 6.4, 6.5; Ch. 7
January 21 Adversarial search: mix-max, alpha-beta, expecti-max (slides) R&N Ch. 5
January 26 Search redux (slides) MDPs, Value Iteration (pdf slides only) to page 26 R&N Ch. 16 thru 16.3; Ch 17 thru 17.2
January 28 MDPs, VI analysis & policy iteration (slides) R&N 17.2 and 17.3
February 2 SSP MDPS; LAO* and LRTDP (review) (slides) M&K Ch 4 thru 4.4.2
February 4 Reinforcement Learning (slides) R&N Ch 21 thru 21.3
February 9 Approximate value functions (slides); Exploration / exploitation & Bandit algorithms (slides - see also 2/11) R&N Sections 21.4 - 21.7; M&K Section 6.2 Tutorial on UCB
February 11 Finish bandits; Deep RL; (slides) UCT (optional: see slides 37+ here) Playing Atari with Deep Reinforcement Learning
February 16 No Class
February 18 Hidden Markov Models (slides) R&N Chapter 15 thru 15.3 (review 13 if necessary)
February 23 HMM Exact Inference & Particle Filters (slides) R&N Sections 15.3 and 15.5
February 25 Bayes Networks (slides) R&N Chapter 14 thru 14.4
March 1 UCT, POMDPs, POMCP (slides) Monte Carlo Planning in Large POMDPs
March 3 POMDPS or BN Inference (slides) R&N Sections 14.4 & 14.5
March 8 No Class
March 10 Grand Finale (plus BN Learning plus NELL) (slides) R&N Chapter 20; [Carlson AAAI-10]

Programming Projects

This quarter, we will use the excellent Berkeley Pac-Man Projects originaly developed by John DeNero and Dan Klein. Please complete the versions listed below, as they differ in places from the originals. Use Python 2.x. Deposit homework in the dropbox.


Check here for info on the final project. Tentative group definitions and a one-sentence idea due TBD. Alternatively, you may complete the individual final programming assignment as the final project.

Take-home Final

Live Wed March 16; due 11:30am on Friday March 18. Open book & static Internet resources, but no discussions with other people besides Dan and Chris.


Course Administration and Policies


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