各种课程建议:面向机器学习(理论基础)的数学

Marc S. Paolella
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引用次数: 0

摘要

鉴于机器学习在许多领域的使用、接受和需求不断增长,尤其是数据科学领域,但也包括金融等其他领域——这在工业界和学术界都是如此,一些大学院系可能希望,或发现自己被迫顺应变革之风,解决这个紧迫的问题。本文件的目的是协助设计相关的课程使用材料在适当的数学水平。它为各种可能的课程对许多可行的书籍进行协议、分类、评估和对比。课程涵盖了线性代数和实分析的几个层次和不同的途径,并对概率论和数学金融中的材料进行了简短的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Various Course Proposals for: Mathematics with a View Towards (the Theoretical Underpinnings of) Machine Learning
In light of the growing use, acceptance of, and demand for, machine learning in many fields, notably data science, but also other fields such as finance- and this in both industry and academics, some university departments might wish, or find themselves forced to, accord to the winds of change and address this pressing issue. The goal of this document is to assist in designing relevant courses using material at the appropriate mathematical level. It protocols, sorts, evaluates, and contrasts, numerous viable books for a variety of possible courses. The subjects span several levels of, and different avenues in, linear algebra and real analysis, with briefer discussions of material in probability theory and mathematical finance.
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