|CSE Home||About Us||Search||Contact Info|
(mausam at cs dot washington dot edu)
Office hours: by appointment, CSE 454
(janara at cs dot washington dot edu)
Office hours: Wednesdays 3-4, CSE 610 (cancelled 4/25 and 5/2)
|Week||Dates||Topics & Lecture Notes||Readings||Supplementary Resources||Advanced Resources|
|1||Mar 26, 28||Introduction, Uninformed Search, Informed Search.||AIMA Chapters 1,3
Depth First Branch and Bound
(Extra reading: Ch. 2)
Applications of AI
Intuition of Search Algorithms
Search Algorithms Performance
Additive Pattern Databases
|2||Apr 2, 4||Local Search, Constraint Satisfaction, Project 1||AIMA 4.1-4.2, 6
Stochastic Beam Search
Guide to Constraint Programming
|3||Apr 9, 11||Constraint Optimization, Logic and Satisfiability||Constraint Optimization,
AIMA 7, 8.1-8.3
(Extra reading: Ch. 9)
Constraint Optimization (Chapter 3)
|4||Apr 16, 18||Advanced Satisfiability, Probability Basics, Bayesian Networks||
Advanced SAT Solvers
|5||Apr 23, 25||Bayes Nets Approximate Inference and Learning, Intro to Machine Learning||
AIMA 14, 20
Metropolis-Hastings Monte Carlo
|6||Apr 30, May 2||Naive Bayes, Logistic Regression, Text Features, Information Retrieval||
Naive Bayes vs. Logistic Regression
Text Processing and Information Retrieval
Naive Bayes vs. Logistic Regression
Probabilistic Modeling for Text Analysis
|7||May 7, 9||
Intro to NLP,
|| AIMA 18.1-18.4, 18.6-18.9
|8||May 14, 16||
Ensembles and Semi-Supervised Learning,
Project 1 Results
|| AIMA 18.10, 2, 10 Ensemble
|9||May 21, 23||
|| AIMA 5.1-5.5, 5.6-5.9, 16.1-16.3, 16.6
How Intelligent is Deep Blue?
General Game Playing
|10||May 30||Markov Decision Processes, Wrap Up|| AIMA 17.1-17.3
||Monte Carlo Planning|
|11||June 7||Final Exam, June 7th, 10:30 am, CSE303||Whole Course
Stuart Russell & Peter Norvig,
Artificial Intelligence: A Modern Approach,
Prentice-Hall, Third Edition (2009) (required).
Mini-projects: 50%; Written Assignments: 10%; Final: 30%; Class Participation: 10%.
There will be two mini-projects (that fit together into one large system):
The gradebook can be found here.
As referenced from Dan Weld's guidelines.
Collaboration is a very good thing. On the other hand, cheating is considered a very serious offense. Please don't do it! 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 Gilligan's Island Rule: This rule says that you are free to meet with fellow students(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. This applies when the assignment is supposed to be an individual effort or whenever two teams discuss common problems they are each encountering (inter-group collaboration). After the meeting, engage in a half hour of mind-numbing activity (like watching an episode of Gilligan's Island), before starting to work on the assignment. This will assure that you are able to reconstruct what you learned from the meeting, by yourself, using your own brain.
- The Freedom of Information Rule: To assure that all collaboration is on the level, you must always write the name(s) of your collaborators on your assignment. This applies when two groups collaborate.