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기계학습은 다양한 수학 분야의 도구들을 사용합니다. 이 문서는 기계학습 개론에 필요한 수학적 배경지식을 간략히 요약하기 위하여 작성되었습니다.

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.

Earlier versions of this document did not include proofs. We have begun adding in proofs where they are reasonably short and aid in understanding. These proofs are not necessary background for CS 189 but can be used to deepen the reader’s understanding. You are free to distribute this document as you wish. The latest version can be found at http://gwthomas.github.io/docs/math4ml.pdf. Please report any mistakes to gwthomas@berkeley.edu

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