Learning Advanced Mathematics behind Machine Learning

Learning Advanced Mathematics behind Machine Learning

In this article, I have shared resources for advanced mathematics courses, which help machine learning. The topics discussed in this article are Convex and Non-Convex Optimization, Information Theory, Probabilistic Graphical Models, etc.

Mathematics forms the basis of most of the machine learning algorithms. Therefore, it is imperative to have a good grasp of mathematics to understand machine learning. While most of the data scientists are aware of basic mathematical concepts such as Linear Algebra, Statistics, etc. but many of them are not aware of some deep mathematical concepts that can help them have a clearer understanding of how an algorithm works or allow them to understand the latest research in machine learning.

In this article, I have shared resources for advanced mathematics courses, which help machine learning. The topics discussed in this article are Convex and Non-Convex Optimization, Information Theory, Probabilistic Graphical Models, etc.

The list of resources is given so that it assumes the reader’s familiarity with basic concepts such as Linear Algebra, Probability Theory, Multivariable Calculus, and Multivariate Statistics. It is vital to understand these essential topics to understand the material presented in the advanced courses present in this article.

I have also written another article on these topics which can act as a predecessor for this article. Feel free to check it out!

mathematics data-science education research machine-learning

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