Fundamentals of Machine Learning in Finance

开始时间: 06/27/2020 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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The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. 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.

金融学中机器学习的基础知识:该课程旨在帮助学生解决现实生活中可能遇到的实际ML问题,这些问题包括:(1)了解问题所在,取决于可用ML的总体情况方法,(2)了解哪种特定的ML方法最适合解决问题,以及(3)成功实施解决方案并评估其性能的能力。 具有一些或几乎没有机器学习(ML)知识的学习者将了解监督和非监督学习以及强化学习的主要算法,并且能够使用ML开源Python包来设计,测试和实现ML算法。在金融。 金融学中的机器学习基础知识将提供对有监督,无监督和强化学习的更深入了解,并最终完成一个使用无监督学习实施简单证券交易策略的项目。 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。





The course aims at helping students to be able to solve practical ML-amenable problems that they may