The notes is mainly based on the following book

• UML: Understanding Machine Learning: From Theory to Algorithms Shai Shalev-Shwartz and Shai Ben-David, 2014.
• PRML: pattern recognition and machine learning Christopher M. Bishop, 2006.
• PGM: Probabilistic Graphical Models: Principles and Techniques Daphne Koller and Nir Friedman, 2009.
• GEV: Graphical Models, Exponential Families, and Variational Inference Martin J. Wainwright and Michael I. Jordan, 2008.

这些笔记主要来自于上述书籍

• The four books are toooooo thick;
• Some valuable or interesting properties are in the exercises. And there is few exercise regarding with pure theory;
• It's beneficial to read the four books together;
• Books of learning theory written(or translated) in Chinese are not satisfied.

• 上述书籍太厚了，需要整理成薄一些的；
• 有些有价值的、有趣的性质，藏在了习题中，而在纯理论部分，完整的习题册是大量匮乏的；
• 上述书籍可以互相关联起来看，融汇贯通；
• 中文世界缺乏翻译得很好的机器学习理论，让人望而却步。

Accomplish in 2021.

2022年写完。同样想写完的，还有一篇小说。

邮箱：zhangsiheng@cvte.com 如果觉得写的还行，可以请我喝咖啡，催更。