If you are planning to get into Machine Learning research, you cannot escape learning Mathematics.

Below is a list of topics that are crucial for learning Machine Learning.

  • Linear Algebra
  • Probability Theory
  • Multivariate Calculus
  • Convex Optimization



Start with Linear Algebra

Learning Resource: Linear Algebra by Prof. Gilbert Strang

Course Description: This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvectors, similarity, and positive definite matrices.

Price: Free

Course Link: Visit the course here

Probability and Measure Theory

Learning Resource: Probability Foundation for Electrical Engineers

Course Description: This course comprises of 49 lectures in Probability Theory by Dr. Krishna Jagannathan, Department of Electrical Engineering, IIT Madras.

Price: Free

Note: Don't get confused with the course name. This course is designed for all those who want an intermediate level understanding in Probability Theory.

Course Link: Visit the course here



Multivariate Calculus

Learning Resources: Multivariable Calculus by Khan Academy

Course Description: This course covers topics such as multivariable functions and their derivatives, applications of multivariable derivatives, integrating multivariable functions, Green's Theorem, Stoke's Theorem, Divergence Theorem, etc.

Price: Free

Course Link: Visit the course here

Convex Optimization

Learning Resource: Applied Optimization for Wireless, Machine Learning, and Big Data

Course Description: This course comprises of 80 video lectures on optimization principles by Prof. Aditya K. Jagannatham, IIT Kanpur.

Price: Free

Course Link: Visit the course here