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I just looked through my bookshelves and here’s what I’ve been through since college (in no particular order other than top to bottom on my shelves):

7 books on general ML (highlights: Murphy’s Machine Learning, Hastie et al’s ESL, Koller&Friedman’s PGMs)

5 on more specialized ML (highlights: Agarwal&Chen’s statistical recommender systems book, Manning&Schutze’s statistical NLP, Settles’ active learning)

13 on stats (highlights: Wooldridge’s econometrics of x-section/panel data, Angrist&Pischke’s econometrics)

4 on numerical methods (highlight: Absil et al’s optimization on matrix manifolds)

4 on CS (highlight: CLRS’s intro to algorithms)

10 on calculus/geometry/topology/algebra (highlights: Bachman’s geometric approach to differential forms, Hestenes&Sobzyk's Clifford algebra to geometric calculus)

8 on fiction writing (highlight: Bickham’s Scene & Structure)

And Rosenberg‘s Nonviolent Communication (not a textbook, but still a highlight worth mentioning).

It amounts to between 3 and 4 per year. Looking back and counting them up, my reaction is holy crap that’s a lot, but that’s kinda the point. Each year it is a reasonable amount of self study. Not nothing, but not anything crazy. Over the course of many years, it adds up to a hell of a lever.



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