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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] 电子书 下载 mobi epub pdf txt

评分☆☆☆☆☆

正好最近在研究，就买了，京东物流还是很给力的！大促的时候都能发货这么速度，点赞！

评分☆☆☆☆☆

正准备学习的机器学习算法与预测算法。正品书

评分☆☆☆☆☆

书已收到，还没来得及看，纸张不错，最后几页还有彩色的插图。

评分☆☆☆☆☆

包装还可以，物流也挺快的，希望可以好好阅读此书，学到优秀技术

评分☆☆☆☆☆

刚买来还没看，挺好的，希望有用

评分☆☆☆☆☆

有塑料膜，书有墨香味，我拿书皮包起来了。

评分☆☆☆☆☆

这本书非常的好，直截了当的几个实践项目，由浅入深，特别适合想要直接上手的同学

评分☆☆☆☆☆

主要介绍了泛化的线性回归方法和提升方法（包括RF、GBDT），内容不错，不过可能由于第一次印刷，还存在很多错误。

评分☆☆☆☆☆

书翻着还不错，应该是正品啦！

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