机器学习界大牛林达华推荐的书籍

最新推荐文章于 2021-04-12 22:43:21 发布

oilover

最新推荐文章于 2021-04-12 22:43:21 发布

阅读量2.1k

点赞数

分类专栏：机器学习文章标签：人工智能机器学习模式识别数学

本文链接：https://blog.youkuaiyun.com/oilover/article/details/51049007

版权

本文列举了机器学习与数学领域的几本重要书籍，包括《模式识别与机器学习》、《统计模式识别》、《学习与核方法》等，适合对机器学习和概率论感兴趣的读者。此外，还推荐了几本编程类书籍，如《结构与解释计算机程序》和《思考C++》，帮助程序员提升编程技能和理解算法。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

Recommended Books

Here is a list of books which I have read and feel it is worth recommending to friends who are interested in computer science.

Machine Learning

Pattern Recognition and Machine Learning

Christopher M. Bishop

A new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Bayesian perspective. It is a must read for people who intends to perform research on Bayesian learning and probabilistic inference.

Graphical Models, Exponential Families, and Variational Inference

Martin J. Wainwright and Michael I. Jordan

It is a comprehensive and brilliant presentation of three closely related subjects: graphical models, exponential families, and variational inference. This is the best manuscript that I have ever read on this subject. Strongly recommended to everyone interested in graphical models. The connections between various inference algorithms and convex optimization is clearly explained. Note: pdf version of this book is freely available online.

Big Data: A Revolution That Will Transform How We Live, Work, and Think

Viktor Mayer-Schonberger, and Kenneth Cukier

A short but insightful manuscript that will motivate you to rethink how we should face the explosive growth of data in the new century.

Statistical Pattern Recognition (2nd/3rd Edition)

Andrew R. Webb, and Keith D. Copsey

A well written book on pattern recognition for beginners. It covers basic topics in this field, including discriminant analysis, decision trees, feature selection, and clustering -- all are basic knowledge that researchers in machine learning or pattern recognition should understand.