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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 08 - Utilities, Markov Decision Processes

Time	Lecture Chapters
[00:00:00]	1. Utilities
[00:34:00]	2. Markov Decision Processes

Go to the Course Home or watch other lectures:

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