StarAi Deep Reinforcement Learning Course

StarAi enrolement is now closed.
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For more information on this initiative, please follow the link below:

https://sites.google.com/view/starai-course-beta/home

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Deep Reinforcement Learning has been responsible for some of the most recent breakthroughs in Machine Learning, from AlphaGo to Dota but also has tremendous commercial application potential. From reducing Google’s data centres cooling costs by 40% to autonomous drone navigation, the applications of RL will continue to increase.

We are extremely proud to announce the release of our StarAi deep reinforcement learning course to the general public.

Commencing in early September, the course will run for approximately 7 weeks, once a week after work hours from 6 - 8pm.

Attendees of the study group can expect to learn:

The Epsilon Greedy Algorithm
Markov Decision Processes & Dynamic Programming
Monto Carlo Sampling, Temporal Difference Learning
Tabular Q methods
Deepminds DQN Algorithm
Policy Gradient Algorithms
Proximial Policy Optimization
Deepmind’s Pysc2 Starcraft 2 API.

What we’re looking for

You are comfortable with basic linear algebra. It’s fine if you have to brush up on these skills before course commencement.

You are comfortable programming in Python (other languages are helpful, but you’ll spend the program writing in Python).

You are comfortable with Git & basic command line calls. eg git push.

We’ll use these criteria for selection:

Impact on you. We want to understand why this course will help you achieve something you couldn’t otherwise.

Self-motivation & communication. We’re looking for people who will work hard through those 7 weeks, and who will inspire others (in the course and externally) to endeavor to learn reinforcement learning as well.

Technical skills. The stronger your technical background, the more time you’ll spend focusing on the reinforcement learning itself.

More information will be emailed out to those who are successful in their application to the course,

Best of luck & regards,
The StarAi team.