Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union, worked at the Institute of Control Sciences in Moscow, then in the US, worked at AT&T, NEC Labs, Facebook AI Research, and now is a professor at Columbia University. His work has been cited over 200,000 times. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman

Most people learn Data Science with an emphasis on Programming. However, to be truly proficient with Data Science (and Machine Learning), you cannot ignore the mathematical foundations behind Data Science. In this post, I present seven books that I enjoyed in learning the mathematical foundations of Data Science. 'Enjoy' is perhaps not the best of words since this effort is hard going! So, why should you undertake the efforts of learning the Maths foundations of Data Science?