Here are some thoughts on stats “vs” ML. Rel btwn the fields, why debates on topic are almost always fundamentally flawed, why I think there’s so much talking past each other, and why I wish we could focus instead on *specific* methods + problems https://sgfin.github.io/2020/01/31/Comments-ML-Statistics/ … 2/3
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To be clear, I’m under no illusion that this post will change anyone’s mind about anything. It was written quickly, and reasonable can disagree with many of my points. Rather, I hope it rids me of desire to waste time on toxic instantiations of this debate in the future. 3/3
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PS Thanks
@JFutoma for prompting me to write it, and for commenting on my raw initial thoughts yesterday.Prikaži ovu nit
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Really really great stuff Sam! I wonder if there is an impending Streisand effect for you though. In attempting to write this to disengage from the twitter debate, you'll end up getting pulled into even more!
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You forgot a major argument though: Take "machine learning" + Rearrange = "Mean Chi learning" Now what field deals with means and Chi tables? STATISTICS!!!! Hence, machine learning == statistical learning! CoNSpiRAcY? You decide!
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Thank you for this! It was incredibly helpful. Do you have any recommendations of articles/books on the history of ML?
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Fantastic question. I wish I had a great answer; most of the history I know I've just picked up through the years googling individual people/methods. Not what you're looking for, but *love* these articles on: Hofstadter: https://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/ … Pitts:http://nautil.us/issue/21/information/the-man-who-tried-to-redeem-the-world-with-logic …
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Many ML methods are rooted in stats and as you say, the field originated as a contrast with non-data-driven approaches. At the end, both stat and ML provide a set of tools for solving problems, we need to care more about the problems that we can solve with them.
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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