Answer

Originally Answered: I do not hold strong maths background, what all should I learn in Maths to be able to be master in Machine Learning and AI?

Andrew Ng, Co-founder of Coursera; Adjunct Professor of Stanford

I think the most important areas of math for machine learning are, in decreasing order:

1. Linear algebra
2. Probability and statistics
3. Calculus (including multivariate calculus)
4. Optimization

After that, I think it falls off quickly. I’ve also found Information Theory helpful. You can find courses on all of these on Coursera or at most universities.

While it’s hard to argue against knowing more math, I think the level of math needed to do machine learning effectively, or to get a PhD in machine learning, has decreased over the years. This is because machine learning has become more empirical (based on experiments) and less theoretical, especially with the rise of deep learning.

As a PhD student I had loved real analysis, and also studied differential geometry, measure theory, and algebraic geometry. While you’re certainly be better off knowing these areas than not, in a world in which you have limited time, consider just spending more time studying machine learning itself, and even studying some of the other technical foundations for building AI systems, such as the algorithms that underly building big data systems and how to organize giant databases, plus HPC (high performance computing).

Best of luck!