Machine learning an algorithmic perspective

1星价 ¥110.9 (8.6折)

2星价￥110.9 定价￥129.0

作者：Stephen Marsland[著]

出版社：世界图书出版公司

本类榜单：计算机/网络

分类：计算机/网络 > 计算机理论

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ISBN：9787519295707
装帧：一般胶版纸
册数：暂无
重量：暂无
开本：24cm
页数：20,437页
出版时间：2022-08-01
条形码：9787519295707 ; 978-7-5192-9570-7

本书特色

本书有两大特点使其成为国际上非常流行的机器学习教材。一是实例来支持理论。本书涵盖了神经网络、图模型、强化学习、进化算法、降维方法及优化等机器学习重要方向。作者在保持学术严谨性和大量堆砌数学公式之间找到了完美的平衡，书中使用基于广泛可用的数据集的实例（并提供Python的代码）来充分展示理论，同时给学有余力的读者指出可在哪找到进一步深入学习的材料用于自学。二是广泛触及各种学科和应用。机器学习的多学科性因其适用于金融、生物学、医学、物理、化学和工程学等领域而得到强调。作者从各种学科中选择实例，并以易于理解的风格编写，弥合了学科之间的鸿沟，实现了理论与实践的理想融合。

内容简介

　　There have been some interesting developments in machine learning over the past four years，since the lst edition of this book came out. One is the rise of Deep Belief Networks as an area of real research interest(and business interest， as large internet-based companies look to snap up every small company working in the area)， while another is the continuing work on statistical interpretations of machine learning algorithms. This second one is very good for the field as an area of research， but it does mean that computer science students， whose statistical background can be rather lacking， find it hard to get started in an area that they are sure should be of interest to them. The hope is that this book， focussing on the algorithms of machine learrung as it does， will help such students get a handle on the ideas，and that it will start them on a journey towards mastery of the relevant mathematics and statistics as well as the necessary programming and experimentation.　　In addition， the libraries available for the Python language have continued to develop，so that there are now many more facilities available for the programmer. This has enabled me to provide a simple implementation of the Support Vector Maclune that can be used for experiments， and to simplify the code in a few other places. All of the code that was used to create the examples in the book is available at http：//stephenmonika.net/(in the &Book' tab)， and use and experimentation with any of this code， as part of any study on machine learning， is strongly encouraged.

rologue Introduction Preliminaries Neurons, Neural Networks, and Linear Discriminants The Multi-Layer Perceptron Radial Basis Functions and Splines Dimensionality Reduction Probabilistic Learning Support Vector Machines Optimisation and Search Evolutionary Learning Reinforcement Learning Learning with Trees Decision by Committee: Ensemble Learning Unsupervised Learning Markov Chain Monte Carlo (MCMC) Methods Graphical Models Symmetric Weights and Deep Belief Networks Gaussian Processes Appendix A. Python

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作者简介

史蒂芬·马斯兰（Stephen Marsland）是新西兰威灵顿维多利亚大学的数学与统计学院教授，他之前在梅西大学任教并担任工程与先进技术学院的研究生教导主任。他是新西兰Te Pūnaha Matatini复杂系统与网络卓越研究中心项目主管，领导复杂性、风险与不确定性等相关主题的研究工作。他是新西兰数学学会的杰出会士，并兼任新西兰数学研究所（NZMRI）所长。

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Machine learning an algorithmic perspective

Stephen Marsland[著]

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