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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/guided-tour-machine-learning-finance
课程评论：没有评论
课程名称：金融机器学习导览 课程概述：本课程旨在提供机器学习领域的入门性和广泛概述，重点关注其在金融领域的应用。课程的最终项目使用监督机器学习方法预测银行关闭情况。同时，尽管本课程可以单独学习，但它也为后续的“金融中的机器学习与强化学习”专业化模块中的主题提供了预览。本课程的目标是让学员理解机器学习是什么，它的用途，以及在金融问题中可以应用到的多种方式。 适合对象： 1. 在银行、资产管理公司或对冲基金等金融机构工作的实践者。 2. 对机器学习在个人日间交易应用感兴趣的个人。 3. 当前在金融、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，想要了解机器学习在金融中的实际应用。 课程要求：完成作业需要具备Python编程经验（包括numpy、pandas和IPython/Jupyter笔记本），以及线性代数、基础概率论和基础微积分知识。 课程大纲： 1. 人工智能与机器学习 2. 机器学习的数学基础 3. 监督学习简介 4. 金融中的监督学习 通过本课程，学员将初步了解机器学习的基本概念和在金融应用中的潜力。
Name:Artificial Intelligence & Machine Learning
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Name:Mathematical Foundations of Machine Learning
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Name:Introduction to Supervised Learning
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Name:Supervised Learning in Finance
Description:
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.
