The code is open source, and available on GitHub.
The main documentation for the site is organized into a couple sections:
机器学习之概率统计基础¶
引言¶
究竟什么是机器学习?¶
A branch of artificial intelligence, concerns the construction and study of systems that can learn from data. (REF)机器学习算法是一类从数据中自动分析获得规律,并利用规律对未知数据进行预测的算法。
概率 vs. 统计¶
概率:研究随机事件出现的可能性的数学分支,描述非确定性(Uncertainty)的正式语言,是统计推断的基础
- 概率:一个事件或事件集合出现的可能性
- 基本问题:给定以一个数据产生过程,则输出的性质是什么。
统计推断:处理数据分析和概率理论的数学分支,与数据挖掘和机器学习是近亲
- 统计量:一个用以描述样本或总体性质的数值,如均值或方差
- 基本问题:给定输出数据,我们可以得到该数据的产生过程的哪些信息(相当于是“概率”的一个逆过程)
概率统计基础的重要性¶
研究数据分析必须大好概率和统计基础
- Using fancy tools like neural nets, boosting and support vector machines without understanding basic statistics like doing brain surgery before knowing how to use a band-aid.
- 做脑外科手术,却不知道怎么使用绷带!
参考书推荐¶
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
- 统计部分涵盖Larry了的内容,本笔记所用的符号体系来自此书。
- Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference
- 中译本:《统计学完全教程》
- 内容很全,但有些部分篇幅略少,更偏向于从统计的角度讲述,为计算机系所写
- 盛聚/谢式千/潘承毅,概率论与数理统计,高等教育出版社
- 浙大的这本很不错,国产书算是不错的。
机器学习之矩阵论¶
Indices and tables¶
Install $project by running:
(1)¶\[e^{i\pi} + 1 = 0\]
Euler’s identity, equation (1), was elected one of the most beautiful mathematical formulas.
Since Pythagoras, we know that \(a^2 + b^2 = c^2\).
\[ \begin{align}\begin{aligned}(a + b)^2 = a^2 + 2ab + b^2\\(a - b)^2 = a^2 - 2ab + b^2\end{aligned}\end{align} \]
way2
\[\begin{split}(a + b)^2 &= (a + b)(a + b) \\ &= a^2 + 2ab + b^2\end{split}\]
way3
\[(a + b)^2 = a^2 + 2ab + b^2\]
Look how easy it is to use $a_a$:
- import project
\[(a + b)^2 = a^2 + 2ab + b^2\]# Get your stuff done project.do_stuff()
\begin{eqnarray}
y & = & ax^2 + bx + c \\
f(x) & = & x^2 + 2xy + y^2
\end{eqnarray}
Contribute¶
- Issue Tracker: https://github.com/iphysresearch/Math_ML/issues
- Source Code: https://github.com/iphysresearch/Math_ML
Support¶
If you are having issues, please let us know. We have a mailing list located at: hewang@mail.bnu.edu.cn
License¶
The project is licensed under the MIT license.