From the game of Go to Kaggle: The story of a Kaggle Grandmaster from Taiwan



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In this interview, we shall know more about his academic background, his passion for Kaggle, and his work as a Data Scientist. Here is an excerpt from my conversation with Kunhao:

Could you tell us about your background and transition from Electrical Engineering to Computer Science and eventually to data science?

Kun-Hao: While pursuing my Bachelors’s, I was majoring in Electrical Engineering(EE) and Computer Science(EECS). Even though I was getting good grades in EE and was also not so good at coding, yet Computer Science(CS) appeared more interesting. Henceforth, I spent more time on CS and even pursued a Master’s degree in computer science.

In graduate school, I was advised by a professor, I-Chen Wu, who mainly worked with RL (Reinforcement Learning) algorithms applied to computer games like Chess, Go, and game 2048. I came to my advisor due to my keen interest in Go and AI . Infact, I even dreamed of becoming a professional Go player but was kept away from their career path by my parents 😃. Artificial Intelligence seemed fancy to me at that time, so I wanted to do research using AI to play Go and beat professionals. Our lab, called the CGI (Computer Game Intelligence) Lab, even won many medals in the Computer Olympics. I was also involved in a 2048 AI project that won several medals in different competitions.

After graduation, I started my first job at Mediatek as a software engineer for 4G and 5G modem chips. Even though I worked in the semiconductor industry, my interest in Machine learning never faded away. Aware that Reinforcement Learning would require some time to be fully applicable in the industry, I started emphasizing Supervised learning instead. I enrolled in courses on Coursera, including Andrew Ng’s — Deep Learning Specialization and even Marios’s( A fellow Kaggle GransMaster at H2O.ai) — Learn from Top Kagglers, to get my fundamentals right. Gradually, I started participating in a lot of Kaggle competitions. The idea was to learn from others while also applying my knowledge to solve real-world problems.

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