Central limit theorem is one of the most fundamental theorems in probability and statistics. The theorem states that sampling distribution of the mean of any independent random variables approaches normal as the sample size increases under certain conditions. Below I created a Shiny application to visualize central limit theorem in effect. Random samples are generated from a selected population distribution to visually assess the distribution of their means against the theoretical asymptotic normal distribution.
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A few months back in March, an AI Go player developed by Google DeepMind surprised many when it won its first match against Sedol Lee, who holds the highest rank in Go. It continued to win four matches out of five winning the series. I made a presentation on AlphaGo for a reading course in data mining for my masters program after digging up articles on the background and the methods. I then continued with programming a Go simulator/game in R.
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