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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☆☆☆☆☆
挺好的 物流很快 就是一张优惠券都用不了
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☆☆☆☆☆
好,很好,非常好,好很好,非常好,非常好!!!!非常好非常好!非常好非常好!
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☆☆☆☆☆
很不错的一本书,就是我看不懂,适合研究生学历的阅读,,,书很好,,,
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☆☆☆☆☆
好评啦啦啦啦啦,全五分,拿京豆。。。。。
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☆☆☆☆☆
没人还没有读,纸质不错,输还算厚重。内容充实,是一本不错的书!看完再来评价一下!
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☆☆☆☆☆
读者只要具有一定的Python编程经验,能够自己安装和使用开源库,就足够了,即使对机器学习一点了解都没有也没关系。本书不会讲机器学习算法背后的数学。
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☆☆☆☆☆
买书如山倒,慢慢啃吧
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☆☆☆☆☆
经典书籍,不用多说啦,很好满意(?ω?)hiahiahia
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