
Book: Mathematics for Machine Learning - nafizh
https://mml-book.github.io/
======
burlesona
It's not clear to me who the target audience is for this book. As someone with
a graduate degree but not a mathematical background, I'm comfortable reading
academic papers, but a lot of this feels over my head. So it reads to me like
it's a quick refresher or reference for people who already learned this stuff
at some point, rather than an introduction. But the preface says this is to be
"a book on Mathematics for Machine Learning that motivates people to learn
mathematical concepts."

As a sidebar, it has always seemed to me that there is a giant gulf between
truly beginner-friendly math books, which are aimed at children, and
introductory math books aimed at adults. The latter almost always read like
foreign language textbooks where you must first know the language before you
can start, while the former are too elementary. I'd love to find "College
level Math for English Majors" or something of the like, if anyone knows of
such a book :)

~~~
jonnybgood
There are some decent intro books for adults out there if you don’t care about
the branch. Honestly, if you’re looking for intro math books you really
shouldn’t care too much about the branch. I think what’s more important is to
develop mathematical thinking.

Here’s some off the top of my head. I really think these will help you build a
good foundation for mathematical thinking.

Concrete Mathematics by Graham, Knuth, Patashnik

Spivak’s Calculus

How to Prove It by Velleman

Polya’s How to Solve It

E.T. Jaynes’ Probability Theory

Conceptual Mathematics by Lawvere and Schanuel

~~~
mhneu
Gil Strang's books are probably even better for an adult with little math
background.

Intro to Linear Algebra:
[http://math.mit.edu/~gs/linearalgebra/](http://math.mit.edu/~gs/linearalgebra/)

Strang does a GREAT job explaining the intuition behind linear algebra. The
book is targeted at first-year undergrads and an adult with just high-school
math could work through it.

Bonus: if you're interested in machine learning, you must learn linear
algebra.

More advanced is this Strang masterpiece: Intro to Applied Mathematics.
[http://bookstore.siam.org/wc02/](http://bookstore.siam.org/wc02/) It covers a
lot of the applied math that we focused on pre-AI and pre-cloud computing.
Diff eqs, diffusion equations, Fourier analysis, numerical methods, phase
plane analysis, optimization, complex anlaysis.

~~~
s3r3nity
I can't recommend Strang enough. I tend to learn better when I can visualize
math concepts, and had such a hard time understanding Linear Algebra until I
finally bought a copy of that book. I love that thing, and it still sits in my
bookshelf years later.

------
dsiegel2275
For those interested in a similar resource, CMU offers a "Mathematics for
Machine Learning" preparatory course each Fall semester. All of the videos
from the 2017 edition are available to watch on YouTube.

Syllabus:
[https://canvas.cmu.edu/courses/603/assignments/syllabus](https://canvas.cmu.edu/courses/603/assignments/syllabus)

YouTube playlist:
[https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z...](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA)

~~~
dufferzafar
The syllabus looks good, but all videos have the same title. Is there a
"topic" -> "video" mapping somewhere? Couldn't find it on the site.

~~~
dsiegel2275
There isn't, unfortunately.

------
AndrewKemendo
Not sure if the authors will read this or not but I beg of you, please put a
table of notation in the forward. The Sutton and Barto Reinforcement Learning
book did that for basically every notation that wasn't basic algebra and it's
been extremely helpful.

Just labeling things I had never seen before, like indicator functions, was
extremely valuable.

Especially for this kind of book that is introducing mathematics to people
from a broad background - I think it's important to understand how much of an
impediment not knowing notation is by sight. Trying to Google or search for
notation is a nightmare.

~~~
bronxbomber92
The authors are open to feedback. They ask that you report it here:
[https://github.com/mml-book/mml-
book.github.io/issues](https://github.com/mml-book/mml-book.github.io/issues)

~~~
chris_wot
I thought this was such a good idea I did it on the parent's behalf:

[https://github.com/mml-book/mml-
book.github.io/issues/33](https://github.com/mml-book/mml-
book.github.io/issues/33)

------
ReactForAll
Here is my feed back, this book appears not to be useful and doesn't seem to
provide any intuition to why we use any of the linear algebra to do work with
machine learning ... it seems like a undergraduate book filled with
descriptions of math. I find there needs to be a bridge to explain why we use
vectors, how we model then relate it back to the math.

------
ghosthamlet
This book is aslo Mathematics for Machine Learning:
[https://github.com/soulmachine/machine-learning-cheat-
sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)

------
breezest
Nowadays, many books cover the elementary mathematics in machine learning.
After I learnt these elementary topics, any good suggestions for computational
learning theory?

~~~
make3
[Deleted]

~~~
foo101
Why delete the comment? Useful context for stochastic_monk's reply is missing
now. You could always preserve the existing and possibly incorrect comment and
append an "Update" or "Edit" section to the comment to override your earlier
comment.

~~~
stochastic_monk
Essentially, he asked if the above poster had read Elements of Statistical
Learning, Murphy's ML textbook, Bishop's PRML, Reinforcement Learning: An
Introduction, and Ian Goodfellow's Deep Learning textbook.

I simply clarified that the question was about computational learning theory,
a subfield largely started by Leslie Valiant in the form of PAC (Probably
Approximately Correct) learning. The difference in emphasis between the
machine learning conferences I mentioned helps point out how practical machine
learning (like ICML, matching PRML/ML/ESL) and feature
extraction/representation learning (like ICLR, perhaps matching portions of
both ICML and ICLR), while important, are not what the previous poster was
asking about.

------
vigdals
TheNewBoston's youtube channel also has some nice math videos that might help
someone. Not sure if its applicable here.
[https://www.youtube.com/watch?v=UxhMTi2bh7k&list=PLAF739DF5F...](https://www.youtube.com/watch?v=UxhMTi2bh7k&list=PLAF739DF5F2D9506C)

------
theCricketer
I've mentioned this on HN before but I think its still relevant to people
interested in learning ML who feel they are behind on the math. If, like me,
you can't sit thru lots of pages of mathematics text and instead prefer that a
human explains it to you via videos that you can replay, here is a list of
courses that take you from basic algebra and pre-calculus math all the way to
the concepts you need to understand the principles behind the most advanced ML
algorithms. All explained by very energetic people who are experts in their
fields, and starting from the very basics.

This covers calculus, linear algebra, probability, statistics, convex
optimization and a math for ML course thrown in for the HN audience:

(The first two are "MOOCs" recorded in the 1970s! probably the first ever
recorded MOOC, even before the internet, and the lecturer is absolute gold)

Calculus Revisited: Single Variable Calculus | MIT
[https://ocw.mit.edu/resources/res-18-006-calculus-
revisited-...](https://ocw.mit.edu/resources/res-18-006-calculus-
revisited-..).

Calculus Revisited: Multivariable Calculus | MIT
[https://ocw.mit.edu/resources/res-18-007-calculus-
revisited-...](https://ocw.mit.edu/resources/res-18-007-calculus-
revisited-..).

Complex Variables, Differential Equations, and Linear Algebra | MIT
[https://ocw.mit.edu/resources/res-18-008-calculus-
revisited-...](https://ocw.mit.edu/resources/res-18-008-calculus-
revisited-..).

Linear Algebra | MIT -
[https://www.youtube.com/watch?v=ZK3O402wf1c&list=PLE7DDD9101...](https://www.youtube.com/watch?v=ZK3O402wf1c&list=PLE7DDD9101..).

Introduction to Linear Dynamical Systems |Stanford
[https://see.stanford.edu/Course/EE263](https://see.stanford.edu/Course/EE263)

Probability | Harvard
[https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6..).

Intermediate Statistics | CMU
[https://www.youtube.com/playlist?list=PLcW8xNfZoh7eI7KSWneVW...](https://www.youtube.com/playlist?list=PLcW8xNfZoh7eI7KSWneVW..).

Convex Optimization I | Stanford
[https://see.stanford.edu/Course/EE364A](https://see.stanford.edu/Course/EE364A)

Math Background for ML | CMU
[https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMW...](https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMW..).

~~~
vecter
These are great resources, but the ultimate approach is wrong. In order to
truly learn math, you _must_ be willing to invest the time and "sit through
lots of pages of mathematics text" and also do a ton of problems, many of
which will take hours and some which will require days of thinking.

I you aren't willing to invest the energy and effort to do that, all the video
watching won't do anything for you. It will get you 5% of the way at best. The
true learning comes from staring at a problem for hours, trying 100 dead ends,
and then finally having an insight two days later while taking a shower that
suddenly make that intractable problem seem trivial.

~~~
mindcrime
_These are great resources, but the ultimate approach is wrong. In order to
truly learn math, you must be willing to invest the time and "sit through lots
of pages of mathematics text" and also do a ton of problems, many of which
will take hours and some which will require days of thinking._

These things aren't mutually exclusive. I don't know about anybody else, but
I'd rather watch a video of a human explaining the subject, _then_ sit down
with a textbook and start working through problems.

~~~
vecter
Of course they're not, but OP kind of implied that it wasn't necessary to read
a lot of pages of mathematics when he said

> you can't sit thru lots of pages of mathematics text

Even if you watch those videos, you still need to sit through lots of pages of
mathematics if you want to master the subject.

