The maths you will need for AI/Machine Learning

This Hacker News thread discusses why and what kind of maths you will need if you pursue AI/Machine learning.

Here is a short summary, and i tend to agree. These where mandatory maths courses when i studied CS :

You need to have a solid foundation in:

Good  to know:

  • Graph theory or Discrete math. (no course on khan academy for that, but on great courses, which isn’t free)

Here are some books:

I like the following quote motivating why you for instance will need calculus:

Calculus essentially discusses how things change smoothly and it has a very nice mechanism for talking about smooth changes algebraically.
A system which is at an optimum will, at that exact point, be no longer increasing or decreasing: a metal sheet balanced at the peak of a hill rests flat.
Many problems in ML are optimization problems: given some set of constraints, what choices of unknown parameters minimizes error? This can be very hard (NP-hard) in general, but if you design your situation to be “smooth” then you can use calculus and its very nice set of algebraic solutions. – Commend by used Tel

It could bee very motivating for students when they first start with calculus, linear algebra and statistics if they have an idea in what fields they can practically use them later on.

Want to refresh your Linear Algebra?

In Machine Learning and especially Deep Learning you will need Linear Algebra. If you have not used your linear algebra in some time, this chapter of the Deep Learning Book will refresh you on the parts of Linear Algebra that are essential to machine Learning.