图书标签: 机器学习 MachineLearning 数据挖掘 ML 计算机科学 计算机 统计学习 Cambridge
发表于2024-11-25
Machine Learning pdf epub mobi txt 电子书 下载 2024
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.
Peter Flach, University of Bristol
models lend the machine learning field diversity, but tasks and features give it unity,这句话可以说是贯穿全书的思想了。而这本书的定位就是a general introduction to machine learning to complement the many more specialist texts。比起一般ml书堆砌算法而言,这本书给给人更加豁然开朗的感觉、更高角度介绍机器学习。
评分models lend the machine learning field diversity, but tasks and features give it unity,这句话可以说是贯穿全书的思想了。而这本书的定位就是a general introduction to machine learning to complement the many more specialist texts。比起一般ml书堆砌算法而言,这本书给给人更加豁然开朗的感觉、更高角度介绍机器学习。
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评分After my final.
评分非常浅显易读,内容很少。
可以看下最后的小结部分,大部分是对基本技术和关键技术的总结,一些论文需要另外学习阅读。 1.树模型和线性模型部分,基本概念都讲得很清楚; 2.距离模型,有些算法,国内的教科书不会提; 3.概率模型,举了朴素贝叶斯分析垃圾邮件的例子; 4.特征部分,写得很好了; 5.集成学...
评分可以看下最后的小结部分,大部分是对基本技术和关键技术的总结,一些论文需要另外学习阅读。 1.树模型和线性模型部分,基本概念都讲得很清楚; 2.距离模型,有些算法,国内的教科书不会提; 3.概率模型,举了朴素贝叶斯分析垃圾邮件的例子; 4.特征部分,写得很好了; 5.集成学...
评分可以看下最后的小结部分,大部分是对基本技术和关键技术的总结,一些论文需要另外学习阅读。 1.树模型和线性模型部分,基本概念都讲得很清楚; 2.距离模型,有些算法,国内的教科书不会提; 3.概率模型,举了朴素贝叶斯分析垃圾邮件的例子; 4.特征部分,写得很好了; 5.集成学...
评分可以看下最后的小结部分,大部分是对基本技术和关键技术的总结,一些论文需要另外学习阅读。 1.树模型和线性模型部分,基本概念都讲得很清楚; 2.距离模型,有些算法,国内的教科书不会提; 3.概率模型,举了朴素贝叶斯分析垃圾邮件的例子; 4.特征部分,写得很好了; 5.集成学...
评分可以看下最后的小结部分,大部分是对基本技术和关键技术的总结,一些论文需要另外学习阅读。 1.树模型和线性模型部分,基本概念都讲得很清楚; 2.距离模型,有些算法,国内的教科书不会提; 3.概率模型,举了朴素贝叶斯分析垃圾邮件的例子; 4.特征部分,写得很好了; 5.集成学...
Machine Learning pdf epub mobi txt 电子书 下载 2024