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CS 188: Introduction to Artificial Intelligence

CS 188: Introduction to Artificial Intelligence (Spring 2015, UC Berkeley). Instructors: Professor Pieter Abbeel and 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 - Agents and Search: Uninformed Search
Lecture 03 - Informed Search: A* Search and Heuristics
Lecture 04 - Graph Search, Constraint Satisfaction Problems
Lecture 05 - Constraint Satisfaction Problems (cont.)
Lecture 06 - Adversarial Search: Game Trees, Minimax
Lecture 07 - Game Trees: Expectimax Search; Utilities
Lecture 08 - Markov Decision Processes
Lecture 09 - Markov Decision Processes (cont.)
Lecture 10 - Reinforcement Learning
Lecture 11 - Reinforcement Learning (cont.)
Lecture 12 - Probability
Lecture 13 - Bayes' Nets I: Representation
Lecture 14 - Bayes' Nets II: Independence
Lecture 15 - Bayes' Nets III: Inference
Lecture 16 - Bayes' Nets IV: Sampling
Lecture 17 - Decision Networks and Value of Perfect Information
Lecture 18 - Hidden Markov Models (HMMs)
Lecture 19 - Particle Filtering and Applications of HMMs
Lecture 20 - Machine Learning: Naive Bayes
Lecture 21 - Machine Learning: Perceptrons
Lecture 22 - Machine Learning: Kernels and Clustering
Lecture 23 - Machine Learning: Decision Trees and Neural Nets
Lecture 24 - Natural Language Processing, Games, and Robotic Cars
Lecture 25 - Games, Computer Vision, and Robotics
Lecture 26 - Conclusion