Transcription of Mathematics for Machine Learning - GitHub Pages
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Mathematics for Machine Learning - GitHub Pages
Mathematics for Machine Learning Garrett Thomas Department of Electrical Engineering and Computer Sciences University of California, Berkeley January 11, 2018. 1 About Machine Learning uses tools from a variety of mathematical fields. This document is an attempt to provide a summary of the mathematical background needed for an introductory class in Machine Learning , which at UC Berkeley is known as CS 189/289A. Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus and linear algebra (at the level of UCB Math 53/54). We emphasize that this document is not a replacement for the prerequisite classes. Most subjects presented here are covered rather minimally;. we intend to give an overview and point the interested reader to more comprehensive treatments for further details.
2 Notation Notation Meaning R set of real numbers Rn set (vector space) of n-tuples of real numbers, endowed with the usual inner product Rm n set (vector space) of m-by-nmatrices ij Kronecker delta, i.e. ij= 1 if i= j, 0 otherwise rf(x) gradient of the function fat x r2f(x) Hessian of the function fat x A> transpose of the matrix A sample space P(A) probability of event A
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