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CS 188: Artificial Intelligence

CS 188: Artificial Intelligence (Fall 2011, UC Berkeley). Instructor: Professor Dan Klein. This course introduces the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. Topics include heuristic search, problem solving, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, expert systems, learning, perception, language understanding.

Lecture 09 - Markov Decision Processes (cont.)


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Lecture 01 - Introduction
Lecture 02 - Queue-Based Search
Lecture 03 - A* Search and Heuristics
Lecture 04 - Constraint Satisfaction Problems
Lecture 05 - Constraint Satisfaction Problems (cont.)
Lecture 06 - Adversarial Search: Game Trees, Minimax
Lecture 07 - Expectimax Search
Lecture 08 - Utilities, Markov Decision Processes
Lecture 09 - Markov Decision Processes (cont.)
Lecture 10 - Reinforcement Learning
Lecture 12 - Probability
Lecture 13 - Bayes' Nets
Lecture 15 - Bayes' Nets III: Inference
Lecture 16 - Bayes' Nets IV: Sampling
Lecture 17 - Midterm Review
Lecture 18 - Decision Diagrams
Lecture 19 - Hidden Markov Models (HMMs): Intro and Filtering
Lecture 20 - HMMs: Particle Filtering
Lecture 22 - Machine Learning (ML): Naive Bayes
Lecture 23 - Perceptrons and More
Lecture 25 - Advanced Applications: Robotics/Vision/Language
Lecture 26 - Advanced Applications: Robotics/Vision/Language (cont.)
Lecture 27 - Conclusion