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How AlphaGo works? 3 Machine Learning Courses for Beginners

KEEP - 27 October 2017

Go Match - AlphaGo vs Lee Sedol

Go match between AlphaGo (left, representative) and Lee Sedol (right),
South Korean professional Go player

Google’s artificial intelligence program AlphaGo has stunned not only the Go community but also every one of us since defeating the top Go players in the world. Thanks to the state-of-the-art machine learning techniques, computers are replacing you and me in more and more complex tasks, say driving, security trading and disease detection to name a few.

Mastering machine learning would require advanced skills in maths, stats and computer science. But don’t be scared - we have picked some introductory courses for those who want to have a touch with this cutting-edge knowledge. Perhaps someday you will invent the next AlphaGo and reshape humanity’s future!

 

Reinforcement learning skills of AlphaGo Zero

Reinforcement learning skills of AlphaGo Zero [1]

 

Machine Learning

https://course.keep.edu.hk/course/49741

This course by Georgia Tech is a fun and introductory course in the format of discussions and chats between the instructors. They will tell you the major techniques of machine learning and describe how they work, without delving deep into mathematical theories.

 

Principles of Machine Learning

https://course.keep.edu.hk/course/164187

This Microsoft course is more practical as the topic is taught with statistical knowledge and computer demonstrations. A basic knowledge in stats and R programming would help, but the course illustrates things in detail so it is still novice-friendly.

 

Machine Learning Foundations---Mathematical Foundations (機器學習基石上)

https://course.keep.edu.hk/course/158987

This course from National Taiwan University writes in English while speaks in Mandarin Chinese. If you are a Chinese learner and prefer studying in a more familiar language, this is the right course for you.

 

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[1] Silver, D. et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354-359. doi:10.1038/nature24270