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Comment by vayarajesh | Hacker News Reader

Is this a good start for Math required for Machine Learning ?

Yes, discrete math is a good start for anything CS, but you'll definitely need more than what's presented here.

No, this is for a general introduction to the mathematics of computer science. This looks like a basic (and good!) text any MIT freshman should be able to master. Perhaps it's for what 6.001 has morphed into?

If you understand this stuff, you really need linear algebra for today's "deep learning", which perhaps is 18.03 (I can no longer remember).

This class is 6.042 and is not required for CS majors. 6.001 morphed into 6.01, which is something to do with python.

18.06 is linear algebra. 18.03 is differential equations, which you don't really need for machine learning.

> 18.06 is linear algebra

Is Gilbert Strang still teaching linear algebra at MIT? His intro materials to all things applied math are incredibly accessible.

No idea. He's definitely getting up there in years, so if he is still teaching now, I don't know how much longer it will be for.

Strang doesn't teach 18.06 every semester anyway.

6.042 fulfills a math requirement for 6-3 (CS) students.

https://www.eecs.mit.edu/curriculum2016

I meant Course 6, which can be 6-1 (EE) or 6-2 (EECS). Given that there are many 6-2 majors out there who say they've majored in computer science, it might come as a surprise to others who don't understand the MIT curriculum that a "computer science" major at MIT does not need to take this class.

Not really. The paper deals with "conventional" CS math. Here are some good resources for ML/DL math.

https://hn.algolia.com/?query=machine%20learning%20math&sort...

Not really. You're better off looking at introductory calculus and statistics. This book places more emphasis on discrete math.

One good way to go about it is to audit an online course [like Andrew Ng's] and figure out what gaps you need to fill in your knowledge to understand the material.

From what starting position are you asking from; what is your current math background?

I am familiar with Linear algebra and at most average understanding of graph theory. I know there is lot more math to cover for ML but I find it overwhelming to start

All you need for introductory machine learning is multivariable calculus (for some simple optimization stuff), linear algebra, and probability. If you don't know probability, here's a good course: https://ocw.mit.edu/courses/electrical-engineering-and-compu....

Once you feel comfortable with those, you'll be more than ready to tackle 6.867: https://ocw.mit.edu/courses/electrical-engineering-and-compu....

In its current state, even cutting-edge machine learning is pretty accessible if you have a good understanding of linear algebra and calculus. If you want to do have a deeper understanding of machine learning then vector calculus, tensors, graph theory, etc. can only help you.

Needs more:

Linear Algebra: http://joshua.smcvt.edu/linearalgebra/

Calculus: https://cnx.org/contents/i4nRcikn@2.48:H2TLb2-S@2/Introducti...

https://cnx.org/contents/HTmjSAcf@2.40:rrzms6rP@2/Introducti...

https://cnx.org/contents/oxzXkyFi@2.49:72YaCFgv@2/Introducti...

I can't thank you enough for this comment

I would be remiss if I didn't also link Paul's Online Math Notes.

http://tutorial.math.lamar.edu/

Also: /r/learnmath

We're friendly!

You're a godsend, thank you from another person. I've been slowly self working through textbooks my friends give me over the years after they finish from their classes but haven't really known which direction to go in being nontraditional.

Don't thank me- thank Jim Hefferon, Paul, and the OpenStax project for having the decency to make these materials available.

Is there a recommended order to these? I skipped two years of math in High School and ended up BSing my way through Calculus without learning any of it, so I'm trying to figure out what I may need to fill the gaps

The calc sequence I presented is 'canonical' and independent of the linear algebra text I posted.

If you're ambitious (or smarter than me) you could tackle both at the same time.

EDIT: By skip two years, what do you mean? Did you miss out on the typical pre-calc/college algebra/trig courses?

Yeah, we had a pre-tests for algebra & trig but I read the textbook before the class started and got 100% on the pre-tests, so they skipped me ahead. In retrospect I regret it.

The OpenStax team has you covered-

College Algebra/Trig: https://cnx.org/contents/E6wQevFf@6.35:nU8Qkzwo@4/Introducti...

Pre-calc: https://cnx.org/contents/_VPq4foj@6.57:vEOnJry_@2/Introducti...

MIT OCW Scholar has those subjects and more with HW, exams, and lecture notes and is intended for autodidacts.

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