
Foundations of ML and AI: Book recommendations - tosh
https://dragan.rocks/articles/18/Programmers-Teach-Yourself-Foundations-of-ML-AI-With-These-X-Books
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dragandj
Original title: Programmer, Teach Yourself Foundations of ML and AI with these
6 Books

Books:

\- Ian Hacking - Introduction to probability and Inductive Logic

\- John Kruschke - Doing Bayesian Data Analysis

\- Gareth Williams - Linear Algebra with Applications, Alternate Edition

\- Matthew Scarpino - OpenCL in Action

\- Michael Nielsen - Neural Networks and Deep Learning

\- Goodfellow, Bengio, and Courville - Deep Learning

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internet555
How to teach yourself ML in two easy steps

1) Have a strong knowledge of undergraduate mathematics, probability,
statistics, numerics

2) read a book about machine learning

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stochastic_monk
Piggybacking on this, I instead recommend an introductory ML book like Bishop
or Murphy, a statistical ML book like Mohri or Shai Shalev-Schwartz, and a
textbook on nonlinear optimization, convex or otherwise.

The jump from classical machine learning to deep learning is not far if you
have a good understanding of first principles.

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basjacobs
Any reason to pick Mohri over Shai Shalev-Schwartz, or the other way around?

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stochastic_monk
I think that SSS is a little easier to follow, while Mohri is more complete.

It was helpful to me to have both, as details skipped over in one proof were
often highlighted or better explained in its corresponding description. For
someone whose training was not theoretical computer science, SSS left me with
a better understanding most of the time.

~~~
basjacobs
Great, I'll start with SSS. Any tips for a good optimization book to read
afterwards?

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ilovecaching
Pattern Recogntion and Machine Learning by Bishop

I’m highly skeptical of lists that do not include this standard text.

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stochastic_monk
PRML was my introductory textbook, and it’s excellent. (The neural network
chapters are a quite good introduction.) I also hear good things about Kevin
Murphy’s text, which brings a more Bayesian approach and is preferred by some
instructors I know, but I’ve never used it.

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mikkyhail
I think that before going into ML and AI it's worth to understand how to
perform the quantitative analysis end-to-end. ML/AI would be just one of the
process steps there, but without the bigger picture, you miss the value of it.

One of the good books to understand the quantitative analysis is "Keeping Up
with Quants" [https://www.amazon.com.au/Keeping-Up-Quants-Understanding-
An...](https://www.amazon.com.au/Keeping-Up-Quants-Understanding-Analytics-
ebook/dp/B00B0SA1LY)

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emmanueloga_
Whatever happened to Norvig's AIMA book [1], did it fall out of fashion? I
don't ever see it mentioned in these kind of posts nowadays.

Perhaps is because it doesn't cover deep learning?

1: [http://aima.cs.berkeley.edu/](http://aima.cs.berkeley.edu/)

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abecedarius
I believe there’s a new edition in prep.

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emmanueloga_
Got it. Found these (old) pointers online:

[https://www.quora.com/When-will-the-4th-edition-of-
Artificia...](https://www.quora.com/When-will-the-4th-edition-of-Artificial-
Intelligence-A-Modern-Approach-be-released)

[https://www.reddit.com/r/artificial/comments/5pzfzs/artifici...](https://www.reddit.com/r/artificial/comments/5pzfzs/artificial_intelligence_a_modern_approach_4th/dg0054h/)

Haven't found any recent updates yet.

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s3f0
Looks cool!

