ML Books Machine Learning (non-DL)

ML Books Machine Learning (non-DL)

2018-07-07. Category & Tags: Machine Learning, Artificial Intelligence, Book

See also:

Favoured #

  • DSML (Kroese, Botev et.al. - Chapman Press 2019) Data Science and Machine Learning: Mathematical and Statistical Methods. With public datasets, code and pdf online.

  • NNDL (Michael Nielsen 2016 邱锡鹏（译） 2020.06) Keywords: CNN, RNN, Attention, Gaussian Mix., RBM, DBN, GAN, RL, TF, PyTorch: equation, pseudocode 伪码, exercise … 公开了所有代码等内容的（中文）电子书。 PDF, PPT, code, exercise & solution, etc.

  • MLAPP (Kevin Murphy), Machine Learning - A Probablistic Perspective, is more comprehensive, insightful and interesting, and contains more “real” examples/problems. However, the presents are kinda out of order, which can be difficult to follow for a first book. image.png

  • PRML (Christopher Bishop, 微软剑桥研究院实验室主任), Pattern Recognition and Machine Learning, is for the ones are very comfortable with calculus/linear algebra, Bayes-oriented, advanced.

  • ESL (Trevor Hastie), Elements of Statistical Learning, 2n ed. Springer 2017. Contains even harder math (ref: zhihu ans. –> point.2), stats.-oriented, advanced. (中文翻译《统计学习要素》、R 代码实现及其习题解答 via Python 数据科学)

Good Text Books (for Master & PhD ?) #

  • Machine Learning by Peter Flach (Cambridge University Press 2012) - The Art and Science of Algorithms that Make Sense of Data. [The examples in this book are clear and easy to understand.]

  • Machine Learning by Tom M Mitchell (McGraw-Hill 1997). (Classical, many original terms and ideas.)

  • PRML. (See “favoured” above)

  • 南京大学 周志华 《机器学习》 (" 西瓜书 “) 清华大学出版社 2016：有伪码，但是比 Peter Flach 的难懂，需要比较熟悉统计语言，旁注格式不如他的另一本书《集成学习》中的 in-line 注释。 类似清华的红皮 PR，没有推导过程，推导可以参考开源南瓜书：西瓜书的公式推导 (“南瓜书 The Pumpkin Book”), by 周志华学生(据说)。

  • MLY (Andrew Ng), Machine Learning Yearning (EN full: ch1-13), 《CN: 机器学习秘籍》(技巧)(CN Web online), (CN Github) and someone’s notes: part 1 (bak), part 2 (bak) and part 3.

  • 动手学深度学习（PyTorch 版）中文版，阿斯顿·张（Aston Zhang），扎卡里·C.立顿（Zachary C. Lipton）， 李沐（Mu Li），亚历山大·J.斯莫拉（Alexander J. Smola），ISBN 9787115600820，人民邮电出版社，2026-02。 image.png.

  • Deep Learning, by Ian Goodfellow (伊恩·古德费洛)、Yoshua Bengio (约书亚·本吉奥)、Aaron Courville (亚伦·库维尔). Deep Learning 中文版 深度学习, “AI 圣经”, ISBN 978-7115461476, 人民邮电出版社， 2017-01-01. Github 开源. image.png.

More Codes #

More Books #

Dated #

  • Programming Collective Intelligence 《集体智慧编程》 2007 O’REILLY by Toby Segaran: Programming selected algorithms from scratch. Good to read when learning some ML courses (e.g. ML by Andrew Ng, Stanford).

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