The goal of «Intro to Reinforcement learning» is in its name: introduce students to reinforcement learning – the prominent area of modern research in artificial intelligence. The reinforcement learning differs much from both supervised and unsupervised learning and is more about how humans learn in reality.
Students will learn from this course both theoretical core and recent practical RL methods. Most importantly, they will learn how to apply such methods to practical problems. In six weeks students will be guided through the basics of Reinforcement Learning (RL): we will talk about essential theory of RL, value-based methods (such as SARSA and Q-learning), policy based algorithms and methods, designed to solve the optimal exploration problem. In addition to algorithms and theory, during the course we will also present useful practical tips and tricks, needed for learning stabilization, and study how to apply the methods to large scale problems with deep neural networks.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.