Guided Tour of Machine Learning in Finance

开始时间: 07/04/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew



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This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

金融机器学习导览:本课程旨在提供对机器学习领域的入门和广泛概述,重点是金融应用。指导性项目中使用了受监督的机器学习方法来预测银行关闭。同时,尽管本课程可以作为一门单独的课程使用,但它可以作为主题的预览,这些主题在金融的机器学习和强化学习专业的后续模块中有更详细的介绍。 金融学中的机器学习导览的目的是了解机器学习的用途,用途以及可以应用多少种不同的财务问题。 本课程针对三类学生而设计: 在银行,资产管理公司或对冲基金等金融机构工作的从业人员 对ML用于个人即日交易的申请感兴趣的个人 目前正在攻读金融,统计学,计算机科学,数学,物理学,工程学或其他相关学科学位的全日制学生,他们想学习ML在金融中的实际应用 要完成本课程的作业,必须具有Python(包括numpy,pandas和IPython / Jupyter笔记本),线性代数,基本概率论和基本微积分的经验。





This course aims at providing an introductory and broad overview of the field of ML with the focus o