Applications of Artificial Intelligence
Instructor: Mausam
Textbook: Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Third edition
Download Slides from here
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Topics & Lecture Notes
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Readings
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Resources and Advanced Readings
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1 |
Introduction, Uninformed Search, Informed Search. |
AIMA Chapters 1,3
(Extra reading: Ch. 2,
Beam Search) |
Video
Anytime A*
Dynamic A* |
2 |
Local Search, Adversarial Search, Computational Voting Theory. |
AIMA 4.1-4.2, 5.1-5.4, 5.7-5.9, 6
(Extra reading: 5.5, 5.6) |
How Intelligent is Deep Blue?
General Game Playing |
3 |
Constraint Satisfaction, Logic and Satisfiability. |
AIMA 6, 7, 8.1-8.3
(Extra reading: Ch. 9) |
Constraint Programming |
4 |
Classical Planning, Agents, Decision Theory |
AIMA 10, 2, 16.1-16.3, 16.6 |
FF Planner Self-driving cars |
5 |
Markov Decision Processes, Probability Basics, Bayesian Networks |
AIMA 17.1-17.4, 13, 14.1-14.4 |
Monte Carlo Planning |
6 |
Bayesian Networks Approximate Inference and Learning, Intro to NLP |
AIMA 14.5, 20.1-20.3 |
Future of Web Search,
IBM Watson Deep QA |
7 |
Guest Lecture: Applications of Modern SAT Solvers (Ashish Sabharwal, IBM Research)
Hidden Markov Models, Intro to Learning, |
AIMA 15.1-15.3, 18.1-18.2, 22.2
(Extra reading: 15.5, 15.4) |
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8 |
Guest Lecture: Learning to Make Music (Sumit Basu, Microsoft Research)
Text Categorization using Naive Bayes |
AIMA 18.3,18.6-18.8 |
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9 |
Decision Trees, Neural Networks, Nearest Neighbor |
AIMA 18.10-18.11 |
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10 |
Ensemble Learning, Unsupervised Learning, Semi-supervised Learning, Wrap-up. |
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Dawn of AI |
