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

Week Dates Topics & Lecture Notes Readings Supplementary Resources Advanced Resources
1 Mar 26, 28 IntroductionUninformed 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
2 Apr 2, 4 Local SearchConstraint SatisfactionProject 1 AIMA 4.1-4.2, 6 Stochastic Beam Search
Genetic Algorithms
Guide to Constraint Programming
Constraint Programming
3 Apr 9, 11 Constraint OptimizationLogic and Satisfiability Constraint Optimization, AIMA 7, 8.1-8.3
(Extra reading: Ch. 9)
Advanced Constraint Optimization (Chapter 3)
4 Apr 16, 18 Advanced SatisfiabilityProbability BasicsBayesian Networks Advanced SAT Solvers
AIMA 13
Phase Transitions Backdoors
5 Apr 23, 25 Bayes Nets Approximate Inference and LearningIntro to Machine Learning AIMA 14, 20 Graphical Models Metropolis-Hastings Monte Carlo
6 Apr 30, May 2 Naive BayesLogistic RegressionText FeaturesInformation 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 NLPDecision TreesLinear Separators AIMA 18.1-18.4, 18.6-18.9
8 May 14, 16 Ensembles and Semi-Supervised LearningAgentsClassical Planning,Project 1 Results AIMA 18.10, 2, 10 Ensemble Classifiers,Co-training Environments FF Planner
9 May 21, 23 Adversarial SearchDecision Theory 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 ProcessesWrap Up AIMA 17.1-17.3 Monte Carlo Planning
11 June 7 Final Exam, June 7th, 10:30 am, CSE303 Whole 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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