机器学习中的数学修炼

· Sonbook Book 200 · 清华大学出版社(崧博)
Ebook
415
Pages

About this ebook

全书共分为两篇:

在上篇中,将从浩瀚的数学海洋中撷取机器学习研究人员最为必须和重要的数学基础。内容主要包括:微积分(含场论)、数值计算和常用最优化方法、概率论基础与数理统计、线性代数等。

在下篇中,将选取机器学习中最为常用的算法和模型进行讲解,这部分内容将涉及(广义)线性回归、图模型(包含贝叶斯网络和HMM等)、分类算法(包括SVM,逻辑回归,神经网络等)和聚类算法(包括K均值和EM算法等)等话题。

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