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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fundamentals-machine-learning-in-finance
课程评论：没有评论
课程名称：金融中的机器学习基础 课程概述： 本课程旨在帮助学生解决实际工作中遇到的机器学习（ML）相关问题，包括：了解所面临的问题在可用的机器学习方法中属于哪个领域，了解哪些具体的机器学习方法最适合解决该问题，以及成功实施解决方案并评估其性能的能力。不论是有无机器学习基础的学习者，都会学习到监督学习、无监督学习和强化学习的主要算法，并能够使用开源Python包设计、测试和实施与金融相关的机器学习算法。 本课程将深入探讨监督学习、无监督学习和强化学习，最终完成一个项目，利用无监督学习实现一个简单的投资组合交易策略。 课程对象： 1. 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。 2. 对机器学习在个人日交易中的应用感兴趣的个人。 3. 在金融、统计学、计算机科学、数学、物理、工程或其他相关学科攻读学位的全日制学生，想要学习机器学习在金融中的实际应用。 完成本课程作业需要具备Python（包括numpy、pandas和IPython/Jupyter notebooks）、线性代数、基本概率论和基础微积分的知识。 课程大纲： - 监督学习基础在金融中的应用 - 无监督学习的核心概念、主成分分析（PCA）与降维 - 数据可视化与聚类 - 序列建模与强化学习
Name:Fundamentals of Supervised Learning in Finance
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Name:Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
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Name:Data Visualization & Clustering
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Name:Sequence Modeling and Reinforcement Learning
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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.
