CS 188: Artificial Intelligence
CS 188: Artificial Intelligence (Spring 2012, UC Berkeley). Instructor: Professor Pieter Abbeel. 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.
Go to the Course Home or watch other lectures:
| Lecture 01 - Introduction |
| Lecture 03 - A* Search and Heuristics |
| Lecture 04 - A* Search and Heuristics (cont.), Constraint Satisfaction Problems |
| Lecture 06 - Search for Games |
| Lecture 07 - Search for Games (cont.) |
| Lecture 08 - Utility Theory, Markov Decision Processes |
| Lecture 09 - Markov Decision Processes (cont.) |
| Lecture 10 - Reinforcement Learning |
| Lecture 11 - Midterm Preparation Lecture |
| Lecture 12 - Reinforcement Learning (cont.) |
| Lecture 13 - Probability |
| Lecture 14 - Probability; Independence, Bayes' Nets |
| Lecture 15 - Bayes' Nets: Representation and Independence |
| Lecture 17 - Bayes' Nets: Sampling |
| Lecture 18 - Midterm Review Lecture |
| Lecture 20 - Hidden Markov Models (HMMs) and Particle Filtering |
| Lecture 21 - HMMs and Particle Filtering (cont.), Speech Recognition |
| Lecture 22 - Decision Diagrams |
| Lecture 24 - Perceptrons |
| Lecture 25 - Perceptrons (cont.) |
| Lecture 26 - Perceptrons (cont.), Advanced Applications: Robotics |
| Lecture 27 - Advanced Applications: Robotics, Computer Vision, Language |
| Lecture 28 - Review: Search, CSPs, Game Trees |
| Lecture 29 - Review: Probability, Bayes' Nets |