内容简介
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
作者简介
Peter Flach has more than twenty years of experience in machine learning teaching and research. He is Editor-in-Chief of Machine Learning and Program Co-Chair of the 2009 ACM Conference on Knowledge Discovery and Data Mining and the 2012 European Conference on Machine Learning and Data Mining. His research spans all aspects of machine learning, from knowledge representation and the use of logic to learn from highly structured data to the analysis and evaluation of machine learning models and methods to large-scale data mining. He is particularly known for his innovative use of Receiver Operating Characteristic (ROC) analysis for understanding and improving machine learning methods. These innovations have proved their effectiveness in a number of invited talks and tutorials and now form the backbone of this book.
精彩书评
"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms."
——Fernando Berzal, Computing Reviews
前言/序言
Machine Learning: The Art and Science of Algorithms that Make Sense of Data [平装] 下载 mobi epub pdf txt 电子书 格式
Machine Learning: The Art and Science of Algorithms that Make Sense of Data [平装] 下载 mobi pdf epub txt 电子书 格式 2024
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有人推荐这本书。买来看一下,纸质书有感觉。
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说的包装很完整,只是还没有打开看,应该还不错吧,这个系列的书买了很多
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老公买的 正在看 还挺不错
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比超市便宜值得购买购买方便快递给力
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商品不错,送货快,包装好,物美价廉,支持京东
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一直想买这本书,刚好搞活动,就买了
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这本书非常好非常基础,顺便把大学数学重新学了一遍。
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书不错,还么就开始看
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举几个例子,我们会介绍怎么把StackOverflow的回答按质量高低进行分类,怎么知道某个音乐文件是爵士风格,还是重金属摇滚风格。另外,本书还涵盖了主题建模、购物习性分析及云计算等高级内容。总之,通过学习本书,读者可以掌握构建自己所需系统的各方面知识,并且学以致用,解决自己面临的现实问题。
Machine Learning: The Art and Science of Algorithms that Make Sense of Data [平装] mobi epub pdf txt 电子书 格式下载 2024