About This Course
When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.
These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.
Reinforcement learning has recently become popular for doing all of that and more.
And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
Learning Objectives
Apply gradient-based supervised machine learning methods to reinforcement learning
Understand reinforcement learning on a technical level
Understand the relationship between reinforcement learning and psychology
Implement 17 different reinforcement learning algorithms
Material Includes
- Videos
- Booklets
Requirements
- Calculus (derivatives)
- Probability / Markov Models
- Numpy, Matplotlib
- Beneficial to have experience with at least a few supervised machine learning methods
Target Audience
- Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning
- Both students and professionals
Your Instructors
