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:

- “Information Theory, Inference and Learning Algorithms” by David MacKaye.
- “Probability Theory: the Logic of Science” by E. T. Jaynes.
- “Elements of Statistical Learning” by Tibshirani is also good.
- “Bayesian Data Analysis” by Andrew Gelman is another great read.
- “Deep Learning” by Ian Goodfellow and Yoshua Bengio

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.