This course provides an introduction to reinforcement learning, which focuses on the study and design of learning agents that interact with a complex, uncertain world to achieve a goal. The course will cover multi- armed bandits, Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the study of intelligence and briefly touch on perspectives from psychology, neuroscience, and philosophy. The course will use the University of Alberta MOOC on Reinforcement Learning. Any student who understands the material in this course will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses, or to apply AI tools and ideas to real-world problems. Prerequisites: CMPUT 175 or 275; one of CMPUT 267, 466, or STAT 265; or consent of the instructor.
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LECTURE B1
(15657) |
155 |
2024-01-08 - 2024-04-12 (MWF)
13:00 - 13:50
CCIS 1-160
|
Primary Instructor: Scott Jordan
|
This course provides an introduction to reinforcement learning, which focuses on the study and design of learning agents that interact with a complex, uncertain world to achieve a goal. The course will cover multi- armed bandits, Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the study of intelligence and briefly touch on perspectives from psychology, neuroscience, and philosophy. The course will use the University of Alberta MOOC on Reinforcement Learning. Any student who understands the material in this course will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses, or to apply AI tools and ideas to real-world problems. Prerequisites: CMPUT 175 or 275; one of CMPUT 267, 466, or STAT 265.
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LECTURE A1
(50730) |
150 |
2024-09-03 - 2024-12-09 (MWF)
13:00 - 13:50
ESB 3-27
|
|
This course provides an introduction to reinforcement learning, which focuses on the study and design of learning agents that interact with a complex, uncertain world to achieve a goal. The course will cover multi- armed bandits, Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the study of intelligence and briefly touch on perspectives from psychology, neuroscience, and philosophy. The course will use the University of Alberta MOOC on Reinforcement Learning. Any student who understands the material in this course will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses, or to apply AI tools and ideas to real-world problems. Prerequisites: CMPUT 175 or 275; one of CMPUT 267, 466, or STAT 265.
| Section | Capacity | Class times | Instructor(s) |
|---|---|---|---|
|
LECTURE B1
(74976) |
150 |
2025-01-06 - 2025-04-09 (MWF)
13:00 - 13:50
CCIS 1-160
|
|