Mathematics for Machine Learning

Some of the main topics needed in machine learning are related to single variable calculus , linear algebra , multivariable calculus , multivariable optimization , probability and statistics and analytic geometry.

Mathematics for Machine Learning

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong: Mathematics for Machine Learning.
Professor Philippe Rigollet - MIT :
Mathematics of Machine Learning.

Linear Algebra

Khan Academy: Linear algebra including videos.
Professor Gilbert Strang - MIT:
Mit Open Course: Linear Algebra including lecture notes, videos and past exams with solutions.
David Cherney, Tom Denton, Rohit Thomas and Andrew Waldron - UCDAVIS:
Linear algebra.

Single and Multivariable Calculus

Prof. David Jerison - MIT: Mit Open Course: Single Variable Calculus including videos, lecture notes and past exams with solutions.
Prof. Denis Auroux - MIT:
Mit Open Course: Multivariable Calculus including videos, lecture notes and past exams with solutions.

Statistics

Professor Joseph Blitzstein - Harvard : Statistics 110: Probability including videos and Notes
Philip B. Stark - University of California, Berkeley :
Statistics including interactive tools included in the materials to help students analyze real datasets.
Jeremy Orloff , Jonathan Bloom - MIT :
Introduction to Probability and Statistics

Machine Learning

Professor Yaser Abu-Mostafa - Caltech : Learning From Data