Mathematics for Machine Learning (Paperback)

Deisenroth, Marc Peter, Faisal, A. Aldo, Ong, Cheng Soon

Mathematics for Machine Learning (Paperback)

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商品描述

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

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  • A one-stop presentation of all the mathematical background needed for machine learning
  • Worked examples make it easier to understand the theory and build both practical experience and intuition
  • Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines

商品描述(中文翻譯)

理解機器學習所需的基本數學工具包括線性代數、解析幾何、矩陣分解、向量微積分、最佳化、概率和統計。這些主題通常在不同的課程中教授，使得數據科學或計算機科學的學生或專業人士難以有效地學習數學知識。這本自成一體的教科書彌補了數學和機器學習教材之間的差距，以最少的先備知識介紹數學概念。它使用這些概念來推導出四種核心機器學習方法：線性回歸、主成分分析、高斯混合模型和支持向量機。對於具有數學背景的學生和其他人來說，這些推導提供了機器學習教材的起點。對於第一次學習數學的人來說，這些方法有助於建立對應用數學概念的直覺和實際經驗。每章都包含了解題示例和練習題以測試理解程度。書籍網站上提供了編程教程。

- 提供機器學習所需的所有數學背景的一站式介紹
- 解題示例使理解理論和建立實際經驗和直覺更容易
- 解釋了核心機器學習方法：線性回歸、主成分分析、高斯混合模型和支持向量機

作者簡介

Marc Peter Deisenroth, University College London
Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London. Prior to this, he was a faculty member in the Department of Computing, Imperial College London. His research areas include data-efficient learning, probabilistic modeling, and autonomous decision making. Deisenroth was Program Chair of the European Workshop on Reinforcement Learning (EWRL) 2012 and Workshops Chair of Robotics Science and Systems (RSS) 2013. His research received Best Paper Awards at the International Conference on Robotics and Automation (ICRA) 2014 and the International Conference on Control, Automation and Systems (ICCAS) 2016. In 2018, he was awarded the President's Award for Outstanding Early Career Researcher at Imperial College London. He is a recipient of a Google Faculty Research Award and a Microsoft P.hD. grant.

A. Aldo Faisal, Imperial College London
A. Aldo Faisal leads the Brain and Behaviour Lab at Imperial College London, where he is faculty at the Departments of Bioengineering and Computing and a Fellow of the Data Science Institute. He is the director of the 20Mio£ UKRI Center for Doctoral Training in AI for Healthcare. Faisal studied Computer Science and Physics at the Universität Bielefeld (Germany). He obtained a Ph.D. in Computational Neuroscience at the University of Cambridge and became Junior Research Fellow in the Computational and Biological Learning Lab. His research is at the interface of neuroscience and machine learning to understand and reverse engineer brains and behavior.

Cheng Soon Ong, Data61, CSIRO
Cheng Soon Ong is Principal Research Scientist at the Machine Learning Research Group, Data61, Commonwealth Scientific and Industrial Research Organisation, Canberra (CSIRO). He is also Adjunct Associate Professor at Australian National University. His research focuses on enabling scientific discovery by extending statistical machine learning methods. Ong received his Ph.D. in Computer Science at Australian National University in 2005. He was a postdoc at Max Planck Institute of Biological Cybernetics and Friedrich Miescher Laboratory. From 2008 to 2011, he was a lecturer in the Department of Computer Science at Eidgenössische Technische Hochschule (ETH) Zürich, and in 2012 and 2013 he worked in the Diagnostic Genomics Team at NICTA in Melbourne.

作者簡介(中文翻譯)

Marc Peter Deisenroth是倫敦大學學院(University College London)計算機科學系的DeepMind人工智慧主席。在此之前，他是倫敦帝國學院(Imperial College London)計算機科學系的教職員。他的研究領域包括高效學習、概率建模和自主決策。Deisenroth曾擔任2012年歐洲強化學習研討會(EWRL)的節目主席，以及2013年機器人科學與系統(RSS)的研討會主席。他的研究在2014年國際機器人與自動化會議(ICRA)和2016年國際控制、自動化和系統會議(ICCAS)上獲得最佳論文獎。2018年，他獲得了帝國學院倫敦校長傑出早期職業研究獎。他還獲得了Google教職研究獎和微軟博士研究獎助金。

A. Aldo Faisal在倫敦帝國學院(Imperial College London)領導著腦與行為實驗室，他是生物工程和計算機科學系的教職員，也是數據科學研究所的研究員。他是2,000萬英鎊的英國研究與創新中心(UKRI)醫療人工智慧博士培訓計劃的主任。Faisal在德國比勒費爾德大學學習計算機科學和物理學。他在劍橋大學獲得了計算神經科學的博士學位，並成為計算和生物學習實驗室的初級研究員。他的研究位於神經科學和機器學習的交界，旨在理解和逆向工程大腦和行為。

Cheng Soon Ong是澳大利亞國家科學和工業研究組織(CSIRO)的機器學習研究小組(Data61)的首席研究科學家，也是澳大利亞國立大學的兼職副教授。他的研究重點是通過擴展統計機器學習方法來實現科學發現。Ong於2005年在澳大利亞國立大學獲得計算機科學博士學位。他曾在馬克斯·普朗克生物控制研究所和弗里德里希·米舍爾實驗室擔任博士後研究員。從2008年到2011年，他在蘇黎世聯邦理工學院(ETH Zurich)的計算機科學系擔任講師，2012年和2013年在墨爾本的NICTA診斷基因組學團隊工作。

目錄大綱

1. Introduction and motivation
2. Linear algebra
3. Analytic geometry
4. Matrix decompositions
5. Vector calculus
6. Probability and distribution
7. Optimization
8. When models meet data
9. Linear regression
10. Dimensionality reduction with principal component analysis
11. Density estimation with Gaussian mixture models
12. Classification with support vector machines.

目錄大綱(中文翻譯)

1. 簡介與動機
2. 線性代數
3. 解析幾何
4. 矩陣分解
5. 向量微積分
6. 機率與分佈
7. 最佳化
8. 當模型遇見數據
9. 線性回歸
10. 利用主成分分析進行降維
11. 利用高斯混合模型進行密度估計
12. 利用支持向量機進行分類。

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