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. Note that this document concerns math background for Machine Learning , not Machine Learning itself. We will not discuss specific Machine Learning models or algorithms except possibly in passing to highlight the relevance of a mathematical concept.
Vectors and matrices are in bold (e.g. x;A). This is true for vectors in Rn as well as for vectors in general vector spaces. We generally use Greek letters for scalars and capital Roman letters for matrices and random variables. To stay focused at an appropriate level of abstraction, we restrict ourselves to real values. In
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