[mmcclellan]

"Background material needed for an undergraduate course has been put in the appendix."

So just being honest, the Appendix is still rather terse and advanced for me. Does anyone have suggestions for prerequisite readings that would help getting someone prepared for this text?

[throw_away_777]

Introduction to statistical learning http://www-bcf.usc.edu/~gareth/ISL/ is a great text for beginners interested in machine learning. It is designed to be accessible (there is a more advanced book covering the same topics) but is still quite comprehensive, in terms of machine learning basics.

[iaw]

Have you taken the standard undergraduate engineering math coursework?

- Calculus covering Derivation, Integration, Multi-Variate

- Linear Algebra

- Differential Equations (May not be relevant here)

'Discrete math' is also useful.

Some of the derivations in the book you can take on faith and not fully prove out to save time, but you should feel 100% comfortable/confident with the notation used.

Let me know what's confusing you and we can try to figure out what you are missing.

[laichzeit0]

No remember this is Hacker News. Most people here learned to code "in a weekend" and they can pick up a new framework "in a weekend". They also see no use wasting money on a 4 year computer science curriculum if you can just learn to program "in a weekend".

Ok I'm being extremely facetious here but it still shocks me when people comment in machine learning or data science threads asking about how they'd go about picking this up but you can clearly tell they have no formal background in the sciences. I guess the only reason it angers me is because if they had just done a CS degree instead of trying to "hack it" none of this would seem like magic.

[1457389]

I'm in this category. I guess my excuse is that I did Bio instead. What's the best way to get up to speed without having to go back to school? I stopped at linear algebra in undergrad but I probably need to refresh that as well. Would appreciate any thoughts you have.

[laichzeit0]

Without getting bewildered I would suggested going through these 3:

1. Book of Proof by Hammack (http://www.people.vcu.edu/~rhammack/BookOfProof/)

2. Calculus by Spivak

3. Linear Algebra Done Right by Axler

Be prepared to work through all (or at the very least only the odd numbered) exercises. If you can't stomach that or find that life gets in the way of you completing even these very basic books, you do not have the time or discipline required to advance in mathematics.

[aoki]

might be worth noting that "calculus by spivak" is famously mistitled and is what people today call an intro analysis text. (says so right in the intro :-)

there are now "warm up" books (alcock) as well as even more basic real analysis books (abbott, about half the length of spivak).

[1457389]

Thank you!

[sn9]

If you make it through all of those, pick up Hubbard and Hubbard's Vector Calculus for a unified treatment of multivariable calculus and linear algebra.

[pkaye]

The first two years of math in any engineering curriculum should cover these topics. Look up the books, lecture materials and videos from various universities online. It is important to try out the homework exercises to absorb the knowledge.

[1457389]

Thanks, I've started on a couple MOOCs but the ones that sound interesting end up being over my head,

[pkaye]

Yeah that is why the coursework is spread over two years to help students absorb the knowledge. You just have to put a lot of time into it.

[greendragon]

Schaum's Outlines are also good if all you really need is a refresher, or to use concurrently with learning the material fresh.

[morenoh149]

try a statistics class and a linear algebra class from coursera.

[1457389]

Thanks!