This course provides an introduction to artificial intelligence, which we define as the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will emphasize agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources. The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information- processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy.
With a focus on AI as the design of agents learning from experience to predict and control their environment, topics will include:
- Markov decision processes
- Reinforcement learning
- Planning by approximate dynamic programming
- Monte Carlo methods
- Heuristic search
- Online supervised learning
The link above is a Google Drive folder belonging to Richard Sutton and Adam White. Access may disappear at any time. Contains the following course materials of interest:
- Assignments (also available in
assignmentsdirectory of this repo) - Exams
- Lecture Videos
- Slides
- Textbooks