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