About this course: Welcome to the Reinforcement Learning course. Here you will find out about: – foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. — with math & batteries included – using deep neural networks for RL tasks — also known as “the hype train” – state of the art RL algorithms — and how to apply duct tape to them for practical problems. – and, of course, teaching your neural network to play games — because that’s what everyone thinks RL is about. We’ll also use it for seq2seq and contextual bandits. Jump in. It’s gonna be fun!
Who is this class for: The course is designed for engineers and scientists, (1) who already know the basics of machine learning and want to broaden their horizons (2) who plan to apply reinforcement learning to their problems, or (3) who want to understand the methods and details standing behind the breaking AI news.
Taught by: Pavel Shvechikov, Researcher at HSE and Sberbank AI LabHSE Faculty of Computer Science
Taught by: Alexander Panin, LecturerHSE Faculty of Computer Science
Course 4 of 7 in the Advanced Machine Learning Specialization
|Commitment||6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section|
|How To Pass||Pass all graded assignments to complete the course.|
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