How do I learn Mathematics for Machine Learning?
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