Mathematics for Machine Learning Deisenroth

产品信息

Author: Marc Peter Deisenroth

Paperback: 398 pages

Publisher: CAMBRIDGE UNIVERSITY PRESS

Language: English

ISBN-10:  110845514X

ISBN-13:  9781108455145

Product Dimensions: 177 x 252 x 18mm

Shipping Weight: 800 g

页面参数仅供参考，具体以实物为准

书籍介绍

本书提供学习所需的基本数学工具包括线性代数、解析几何、矩阵分解、向量演算、优化、概率和统计。这些主题传统上是在不同的课程中教授的，这使得数据科学或计算机科学的学生或专业人员很难有效地学习数学。这自成一体的教科书桥梁之间的差距，数学和机器学习文本，介绍数学概念与*小的先决条件。利用这些概念推导出四种中央机器学习方法:线性回归、主成分分析、高斯混合模型和支持向量机。对于具有数学背景的学生和其他人来说，这些推导为机器学习文本提供了一个起点。对于第一次学习数学的学生，这些方法有助于建立应用数学概念的直觉和实践经验。每一章都包括工作的例子和练习来测试理解。

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.

编辑推荐

“这本书为机器学习提供了所有基本的数学概念。我期待着与学生、同事以及任何有兴趣巩固基础的人分享。”—— 麦吉尔大学

'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' —— McGill University

作者介绍

马克·彼得是伦敦大学学院计算机科学系DeepMind人工智能系主任。在此之前，他是伦敦帝国理工学院计算机系的教员。他的研究领域包括数据效率学习、概率建模和自主决策。Deisenroth是2012年欧洲强化学习研讨会(EWRL)和2013年机器人科学与系统研讨会(RSS)的项目主席。他的研究获得了2014年国际机器人与自动化会议(ICRA)和2016年国际控制、自动化与系统会议(ICCAS)的最佳论文奖。2018年获伦敦帝国理工学院总统杰出早期职业研究员奖。另外，他是谷歌教员研究奖和微软P.hD奖的获得者。

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.