Artificial Intelligence
CS 5300/CS 6300
Spring 2009
Instructor: Hal Daume III: me AT hal3 DOT name
Office Hours: MEB 3126; by appointment
Schedule: TBA
Location: TBA
Mailing list: Cs5300 -- PLEASE subscribe (but don't post)!
teach-cs5300 (post questions here)
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Jump to: [Syllabus] [Homework] [Pacman Competition] [Course Policies]

 Background and Description


 Grading


 Textbook

The official textbook for this course is:

Artificial Intelligence: A Modern Approach (Second Edition)
by Stuart Russell and Peter Norvig.
Prentice Hall, 2002.

Be sure you have the Second Edition (it is GREEN, not BURGUNDY): the first edition is not sufficient.

We will also occasionally have readings from:

Reinforcement Learning: An Introduction
by Richard S. Sutton and Andrew G. Barto.
MIT Press, 1998.

This book is available online.


 Syllabus (tentative)

The following syllabus is subject to change, but likely not by very much. The readings listed are readings that you should have finished by that date. Warning: unlike many classes you're probably used to (in CS), the lectures are not designed to regurgitate what you should have learned in the book. This means that if you don't come prepared, you may lose out!.

Homework assignments are due by before class on the date listed on the syllabus. Programming assignments are to be completed in Python.

Date Topics Readings HW Notes
Day 1 Introduction to AI - - -
Agents
Day 2 Agents
Depth and breadth first search 		- - -
Day 3 Agents II
A* Search and Heuristics 		- P0 -
Day 4 Constraint Satisfaction
Search and iterative algorithms 		- - -
Day 5 Constraint Satisfaction II
Tree-structured CSPs and more search 		- - -
Day 6 Robot Motion
Motion planning and decompositions 		- P1 -
Day 7 Game Playing
Minimax search 		- - -
Day 8 Game Playing II
Expectimax search 		- - -
Reinforcement Learning
Day 9 Utility
Consistency and risk 		- - -
Day 10 Markov Decision Processes
Value iteration 		- - -
Day 11 Markov Decision Processes II
Policy iteration and TD-learning 		- P2 -
Day 12 Reinforcement Learning
Exporation/exploitation, Q-learning 		- - -
Day 13 Reinforcement Learning II
Feature-based respresentations 		- - -
Day 14 Reinforcement Learning III
Policy search 		- - -
Probabilistic Belief Networks
Day 15 Probability
Everything you need to know! 		- P3 -
Day 16 MIDTERM - - -
Day 17 Bayes' Nets
Graphical models and conditional independence 		- - -
Day 18 Bayes' Nets II
Causality 		- - -
Day 19 Bayes' Nets III
Inference by enumeration, variable elimination 		- - -
Day 20 Bayes' Nets IV
Markov Chain Sampling 		- - -
Day 21 Decision Diagrams
Value of information, Markov chains 		- - -
Day 22 HMMs
Monitoring and robot localization 		- - -
Day 23 HMMs II
Particle filtering and resampling 		- - -
Day 24 HMMs III
Linear space models: Kalman filtering 		- P4 -
Applications
Day 25 Speech
Viterbi and acousting modeling 		- - -
Day 26 POMDPs
Agents under uncertainty 		- - -
Day 27 Machine learning
Classification: kNN and perceptron 		- P5 -
Day 28 Machine learning II
Clustering: k-means and hierarchical 		- - -
Day 29 Last Day Party
Advanced topics and Pacman contest 		- - -

 Homework Assignments

See the syllabus above for due dates. Please see the handin instructions. Homeworks are all equally weighted.

Programming projects more substantial and to be completed in Python. The projects are:


 Pacman Competition


 Course Policies

Cheating: Any assignment or exam that is handed in must be your own work. However, talking with one another to understand the material better is strongly encouraged. Recognizing the distinction between cheating and cooperation is very important. If you copy someone else's solution, you are cheating. If you let someone else copy your solution, you are cheating. If someone dictates a solution to you, you are cheating. Everything you hand in must be in your own words, and based on your own understanding of the solution. If someone helps you understand the problem during a high-level discussion, you are not cheating. We strongly encourage students to help one another understand the material presented in class, in the book, and general issues relevant to the assignments. When taking an exam, you must work independently. Any collaboration during an exam will be considered cheating. Any student who is caught cheating will be given an E in the course and referred to the University Student Behavior Committee. Please don't take that chance - if you're having trouble understanding the material, please let us know and we will be more than happy to help.

ADA: The University of Utah conforms to all standards of the Americans with Disabilities Act (ADA). If you wish to qualify for exemptions under this act, notify the Center for Disabled Students Services, 160 Union.

College guidelines: Document concerning adding, dropping, etc. here.