非常精彩的對談，看得出來訪問者做了很多功課，發問和回應都極有見地，不過還是有評論者認為他是 Horrible interviewer，個人以為這個評論絕對是過分了。
用 k-means 演算法弄了一個 image sementation 的範例，順手把農曆年假期間去南寮海邊拍的照片當作測試標的，把照片降階之後，赫然發現只剩下兩個顏色的照片別有一番味道，不輸全彩的原始照片。
Image Source: Wikipedia
這就是牛人和凡人的差距嗎？Pedro 玩電玩想到 NP-Complete，我輩玩俄羅斯方塊，想到什麼？
Machine learning is all about predictions, supervised learning, unsupervised learning, etc.
Statistics is about sample, population, hypothesis, etc.
然後 Astash Shah 說統計是數學的分枝科目，而機器學習的理論技術則是源自人工智慧。
Machine learning is a subfield of computer science and artificial intelligence. It deals with building systems that can learn from data, instead of explicitly programmed instructions.
A statistical model, on the other hand, is a subfield of mathematics.
ML professional: “The model is 85% accurate in predicting Y, given a, b and c.”
Statistician: “The model is 85% accurate in predicting Y, given a, b and c; and I am 90% certain that you will obtain the same result.”
The difference between the two has reduced significantly over the past decade. Both the branches have learned from each other a lot and will continue to come closer together in the future.
But, understanding the association and knowing their differences enables machine learners and statisticians to expand their knowledge and even apply methods outside their domain of expertise. This is the notion of “data science” itself, which aims to bridge the gap.