# Some resources to start with Fundamentals of Machine Learning

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With a number of courses, books and reading material out there here is a list of some which I personally find useful for building a fundamental understanding in Machine Learning.

Machine Learning at a higher level requires some mathematical prerequisites which are at the heart of it.

- Learning Theory
- Optimization
- Statistical learning and high dimensional probability theory

Some really nice resources might be the ones below

- Learning Theory
- Learning from Data - Caltech.
- The initial chapters from Foundations of Machine Learning - Mohri, or Part I from Understanding Machine Learning From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David.

- Optimization for Machine Learning
- Large scale optimization for Machine Learning - Talks by Suvrit Sra - Part 1, Part 2, and Part 3 - Slides.
- Convex Optimization literature - Convex Optimization course by Stephen Boyd Slides, and the classical book on Introductory Lectures on Convex Programming - Yuri Nesterov.
- Non-convex Optimization for Machine Learning - Jain and Kar.
- OPTML++ page by Suvrit Sra.

- Statistical Learning and Probabilistic Machine Lerning

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