Graduate Course in Artificial Intelligence – Dr. Mausam [Playlist]

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Professor: Dr. Mausam  – (University of Washington) taught the graduate AI class in autumn 2012. These videos are recordings of most of the classes.
TA: Janara Christensen 

Schedule

WeekDatesTopics & Lecture NotesReadingsSupplementary ResourcesAdvanced Resources
1Mar 26, 28IntroductionUninformed SearchInformed Search.AIMA Chapters 1,3
Beam Search
Depth First Branch and Bound
IDA*
(Extra reading: Ch. 2)
Applications of AI
Intuition of Search Algorithms
Search Algorithms Performance
Pattern Databases
Anytime A*
Additive Pattern Databases
2Apr 2, 4Local SearchConstraint SatisfactionProject 1AIMA 4.1-4.2, 6Stochastic Beam Search
Genetic Algorithms
Guide to Constraint Programming
Constraint Programming
3Apr 9, 11Constraint OptimizationLogic and SatisfiabilityConstraint Optimization, AIMA 7, 8.1-8.3
(Extra reading: Ch. 9)
Advanced Constraint Optimization (Chapter 3)
4Apr 16, 18Advanced SatisfiabilityProbability BasicsBayesian NetworksAdvanced SAT Solvers
AIMA 13
Phase TransitionsBackdoors
5Apr 23, 25Bayes Nets Approximate Inference and LearningIntro to Machine LearningAIMA 14, 20Graphical ModelsMetropolis-Hastings Monte Carlo
6Apr 30, May 2Naive BayesLogistic RegressionText FeaturesInformation RetrievalNaive Bayes vs. Logistic Regression
Text Processing and Information Retrieval
Naive Bayes vs. Logistic RegressionProbabilistic Modeling for Text Analysis
7May 7, 9Intro to NLPDecision TreesLinear SeparatorsAIMA 18.1-18.4, 18.6-18.9
8May 14, 16Ensembles and Semi-Supervised LearningAgentsClassical Planning,Project 1 ResultsAIMA 18.10, 2, 10 Ensemble Classifiers,Co-trainingEnvironmentsFF Planner
9May 21, 23Adversarial SearchDecision TheoryAIMA 5.1-5.5, 5.6-5.9, 16.1-16.3, 16.6How Intelligent is Deep Blue?General Game Playing
10May 30Markov Decision ProcessesWrap UpAIMA 17.1-17.3Monte Carlo Planning
11June 7Final Exam, June 7th, 10:30 am, CSE303Whole Course

Textbook

Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach,
Prentice-Hall, Third Edition (2009) (required).

Grading

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.

Bio:

Mausam graduated with his PhD in 2007 and joined the Turing Center at the University of Washington as a Research Assistant Professor. His research explores several threads in artificial intelligence, including scaling probabilistic planning algorithms, large-scale information extraction over the Web, panlingual machine translation and enabling complex computation over crowd-sourced platforms. His PhD dissertation received honorable mention for the 2008 ICAPS Best Dissertation Award awarded to the best AI Planning and Scheduling dissertation of the two previous years. He had earlier received his B.Tech. from Indian Institute of Technology, Delhi in 2001. For more information, click here.

 

(Source: YouTube | Pröf Mausam)

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