For the people who are interested in ML, the thing to remember here is that he is a Serious mathematician, and he values rigor and in-depth understanding above all. A lot of his three star homework problems were basically impossible. He writes books first and foremost so he can understand things better. In math books, there's the book you first read when you don't understand something, then the book you read when you understand everything. This is book in the link.

for linear algebra, this:https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)

"In the following four chapters, the basic algebraic structures (groups, rings, fields, vectorspaces) are reviewed, with a major emphasis on vector spaces. Basic notions of linear algebra such as vector spaces, subspaces, linear combinations, linear independence, [...], dual spaces,hyperplanes, transpose of a linear maps, are reviewed."

If anyone needs to start even earlier than this, I've actually found "3D Math Basics for Graphics and Game Development" to be a good true intro for linear algebra-related stuff. I think this would probably hold even if your primary interest is something other than graphics/game dev. Some of the text in that book's intro is a little cringey with its reliance on kind of juvenile game references, but I didn't find that sort of writing continuing during the actual text. So just push past that stuff.

I got a copy of it to act as a refresher before diving into Real-Time Collision Detection since it's been quite a long time since formal math for me (as in, high school, because I'm self-taught in CS). I've managed to make up a lot of ground by working hard and finding classes to audit online (Strang's linear alg course on OCW is a good one), but I have found that depressingly few math texts which claim to be "introductory" are actually truly introductory.

This isn't a slight against the linked work, I absolutely love when profs make resources such as this freely available.

"How to Prove It" and "Book of Proof" are also great intros to formal math, if less immediately practical.

Keep in mind it can take an hour, and sometimes way more, to really absorb a single page of a math book like this (do the math). This is more of a reference text.

I think it's a good time to mention a couple of nice books (related)

1. Elementary intro to math of machine learning [0]. Its style is a bit less austere than that of OP's. It also has a chapter on probability. It could possible serve as a great prequel to the book linked in the OP.

2. The book on probability related topics of general data science: high-dimensional geometry, random walks, Markov chains, random graphs, various related algorithms etc [1]

3. Support for people who'd like to read books like the one linked in the OP, but never seen any kind of higher math before [2]. This book has a cover that screams trashy book extremely skimpy on actual info (anyone who reads a lot of tech books knows what I am talking about), but surprisingly,it contains everything it says it does and in great detail. Not even actual math textbooks (say, Springer) are usually written with this much detail. Author likes to add bullet point style elaboration to almost every definition and theorem which is (almost) never the case with gazillions of books usually titled "Abstract Algebra", "Real Analysis", "Complex Analysis" etc. Some such books sometimes attach words like "friendly" to their title (say, "Friendly Measure Theory For Idiots") and still do not rise to the occasion. Worse yet, a ton (if not most) of these books are exact clones of each other with different author names attached. The linked book doesn't suffer from any of these problems.

[0] Mathematics For Machine Learning by Deisentoth, Faisal, Ong

https://mml-book.github.io/book/mml-book.pdf

[1] Foundations Of Data Science By Blum, Hopcroft, Kannan

http://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March%...

2] Pure Mathematics for Beginners: A Rigorous Introduction to Logic, Set Theory, Abstract Algebra, Number Theory, Real Analysis, Topology, Complex Analysis, and Linear Algebra by Steve Warner

https://www.amazon.com/Pure-Mathematics-Beginners-Rigorous-I...

So many teachers seem incapable of stepping outside their sphere of knowledge and seeing what they know and others do not. And so much work went into this.

This IS a lot of math (1,962 pages) and it’s missing a preface/introduction which would have been helpful to understand if I need to go linear or if a la carte is okay. At the moment I’d assume each major section is independent.

Awesome find! Wonder how It’s used. (One of) the author(s) seems pretty prolific too - http://www.cis.upenn.edu/~jean/

That being said, this is faaaaaar beyond basics. It'd be more appropriate to call this an incomplete (aiming to be comprehensive) guide to almost everything you need to know in computer science (related to math).

I remembered that I took an advanced course about Bayesian Inference, and one course about Multivariate Statistics (PCA, Factor analysis, these kind of things), and my project is about Bernstein Polynomial. That's it...

The point is, understanding integrals and derivatives doesn't require one to memorize all the mechanical rules. Using software to compute those functions can be a huge time saver. No one should go with pen an paper double checking if that polynomial integral is correct or not!

With a book almost 2000 pages long, I wonder if this books leans more heavily on the mechanical-rules side of math. In my mind, is the difference between writing a book such that you can write your own wolfram alpha, or writing a book so you can just use it.

I suspect there are better resources for each topic covered (e.g Gilbert Strang books and OCW lectures for Linear Algebra), but it is definitely interesting to peruse and get a sense of relevant topics.

It's 2000 pages long....

[Reads the first paragraph of the 2nd chapter]

Me: I don't know anything about math. At all.

Any hope of that happening?

But I would be shocked if this would be of any use for someone trying to learn a little linear algebra in order to play with neural networks. For that I think you still want Strang.

I think "foundations" might have been a better word than "basics" here. "Basics" in any case is not in the printed title, only in the filename.

I feel like it's some kind of misguided intellectual humility. Kind of feels vaguely related to how so many Haskell packages are version "0.*".

To me basics mean that if you study this entire book you won’t be able to understand ML otherwise it would say comprehensive. Furthermore the math presented in this book are all taught in 1st year courses for most CS programs I’ve encountered.

> Keep in mind it can take an hour, and sometimes way more, to really absorb a single page of a math.

Learning is a personal experience and happens at differing rates for different people. While I do agree this book is rather terse and would serve as a good reference any added explanations around the proofs would force a split to multiple publications so I can see why the authors chose to present it in the way they did.

Overall I have found this text easy to digest and well formulated and thank the authors and poster.

It definitely looks more like, "math basics" for x field. Kinda like "automotive basics" for Honda or Ford vehicles. Where it's presumed that you know a lot of automotive lingo to begin with and you just need to know what spark plugs go with what engine. And not, "what is a spark plug?"

If you look at any of the later chapters that are trying to teach something new, they are much more gentle and motivate the topic of that chapter: see e.g. “24.1 Affine Spaces” on page 759, or “26.1 Why Projective Spaces?” on page 823, etc.

Other chapters that are meant as a review are similarly terse and quick to the point (like Chapter 2), e.g. Chapter 37 “Topology” on page 1287.

I think it's good when books make conscious choices about what they're teaching versus assuming as a prerequisite (and communicate it to the reader, by using terms like “reviewed” — presumably the yet-to-be-written Introduction chapter will also mention this more explicitly).

https://www.amazon.com/gp/product/1466230525 - Mathematical Notation: A Guide for Engineers and Scientists

Then he wants to define, say, addition of real numbers. So, given two real numbers, x and y, that might be equal, he wants to define x + y.

So, here he wants to regard addition, that is, +, as an operation. Then, as is usual for defining operations, he wants an operation to be just a special case of a function. So, he wants to call + a function. So, + will be a function of two variables, say, x and y. With usual function notation we will have

+(x,y) = x + y

The set of all (x,y) is the domain of the function, and the set of all x + y is the range.

So, that defines the function + except commonly in pure math we want to be explicit about the range and domain of the function.

For function +, the range is just the set of all pairs (x,y) with x and y in R. That set is also the set theory Cartesian product of set R with itself and written R x R. So, the domain of + is R x R. The range is just R. Then to be explicit about the range and domain of function +, we can write

+: R x R --> R

which says that + is a function with range R x R and domain R.

We learned how to add in, what, kindergarten? So, why make this so complicated?

Well, he wants to regard the real numbers as just one example of lots of different algebraic systems, e.g., groups, fields, vector spaces, and much more, with lots of operations and, possibly, more that could be defined. E.g., later in his book he will want to add vectors and matrices, take an inner product of two vectors, and multiply two matrices.

So, back to addition on the real numbers, he wants to regard that as just a special case of an operation on an algebraic system.

IMHO there's not much benefit for making adding two real numbers look so complicated.

Whatever he did in that chapter for defining addition on the reals, soon he is discussing matrix multiplication with no definition at all -- assuming the reader already understands that, that is defined and discussed many pages later in his book.

So, in his notation

+: R x R --> R

and matrix multiplication, he is using material before he has defined it, even before he has motivated, explained, exemplified, indicated the value of, and defined it. In good math writing and in good technical writing more generally, that practice is, in non-technical language, a bummer.

But from the table of contents, it appears that the book has quite a long list of possibly interesting narrow topics. And maybe for the routine material, his proofs and presentation are good -- maybe. I thought enough of the book to keep a copy of the PDF. It's there; if someday I want a discussion of some narrow topic, maybe I'll try his book!

In mathematical writing, it used to be common for the word processing to be much more work than the mathematics! Now with TeX and LaTeX, and I'm assuming that the book used one of these two, the flood gates are open!

Once you have spent countless hours doing exercises to the extent that you understand the math, you already remember the rules. If you have not spent countless hours doing exercises, you don't understand anything at this level.

You don't hire a programmer who has read all the books and 'understands' programming but has never programmed. It's the same with math. You don't just read a math book from start to finish. You can use wolfram alpha for visualizing functions, not for learning math.

Hm - maybe I'm fortunate that I studied calculus before there were (accessible) software packages that could just do this stuff for you, because back then, the only way to solve these was to do them on paper. I'm sure I would have been tempted to just "skip ahead" to letting the computer do it for me, but I definitely learned a lot more going through all of the steps myself than I would have if I had just gotten a high-level understanding of what was going on and plugged the rest into a computer. Because, honestly, integrating polynomials is really, really easy - if you know how to do it, you can do it on paper faster than you can load up wolfram-alpha, type it in, and wait for an answer.