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.
本课程是 Machine Learning and Reinforcement Learning in Finance 专项课程 专项课程的一部分
Guided Tour of Machine Learning in Finance
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课程信息
35,847 次近期查看
学生职业成果
50%
完成这些课程后已开始新的职业生涯
47%
通过此课程获得实实在在的工作福利
学生职业成果
50%
完成这些课程后已开始新的职业生涯
47%
通过此课程获得实实在在的工作福利
100% 在线
立即开始，按照自己的计划学习。
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中级
完成时间大约为21 小时
建议：11 hours/week
英语（English）
字幕：英语（English）, 法语（French）
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Artificial Intelligence & Machine Learning
完成时间为 3 小时
11 个视频 （总计 75 分钟）, 3 个阅读材料, 1 个测验
11 个视频
Welcome Note4分钟
Specialization Objectives8分钟
Specialization Prerequisites7分钟
Artificial Intelligence and Machine Learning, Part I6分钟
Artificial Intelligence and Machine Learning, Part II7分钟
Machine Learning as a Foundation of Artificial Intelligence, Part I5分钟
Machine Learning as a Foundation of Artificial Intelligence, Part II7分钟
Machine Learning as a Foundation of Artificial Intelligence, Part III7分钟
Machine Learning in Finance vs Machine Learning in Tech, Part I6分钟
Machine Learning in Finance vs Machine Learning in Tech, Part II6分钟
Machine Learning in Finance vs Machine Learning in Tech, Part III8分钟
3 个阅读材料
The Business of Artificial Intelligence30分钟
How AI and Automation Will Shape Finance in the Future30分钟
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130分钟
1 个练习
Module 1 Quiz30分钟
完成时间为 6 小时
Mathematical Foundations of Machine Learning
完成时间为 6 小时
6 个视频 （总计 45 分钟）, 3 个阅读材料, 2 个测验
6 个视频
The No Free Lunch Theorem7分钟
Overfitting and Model Capacity8分钟
Linear Regression7分钟
Regularization, Validation Set, and Hyper-parameters10分钟
Overview of the Supervised Machine Learning in Finance3分钟
3 个阅读材料
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41小时
Leo Breiman, “Statistical Modeling: The Two Cultures”1小时
Jupyter Notebook FAQ10分钟
1 个练习
Module 2 Quiz15分钟
完成时间为 6 小时
Introduction to Supervised Learning
完成时间为 6 小时
7 个视频 （总计 75 分钟）, 4 个阅读材料, 2 个测验
7 个视频
A First Demo of TensorFlow11分钟
Linear Regression in TensorFlow10分钟
Neural Networks11分钟
Gradient Descent Optimization10分钟
Gradient Descent for Neural Networks12分钟
Stochastic Gradient Descent8分钟
4 个阅读材料
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1小时
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15分钟
J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-4115分钟
Jupyter Notebook FAQ10分钟
1 个练习
Module 3 Quiz15分钟
完成时间为 10 小时
Supervised Learning in Finance
完成时间为 10 小时
9 个视频 （总计 66 分钟）, 4 个阅读材料, 3 个测验
9 个视频
Fundamental Analysis7分钟
Machine Learning as Model Estimation8分钟
Maximum Likelihood Estimation10分钟
Probabilistic Classification Models6分钟
Logistic Regression for Modeling Bank Failures, Part I8分钟
Logistic Regression for Modeling Bank Failures, Part II5分钟
Logistic Regression for Modeling Bank Failures, Part III8分钟
Supervised Learning: Conclusion2分钟
4 个阅读材料
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31小时
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1小时
Jupyter Notebook FAQ10分钟
Jupyter Notebook FAQ10分钟
1 个练习
Module 4 Quiz21分钟
审阅
3.8
来自GUIDED TOUR OF MACHINE LEARNING IN FINANCE的热门评论
由 KD 提供Aug 23, 2019
Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.
由 VB 提供Aug 25, 2018
More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.
由 MS 提供Dec 31, 2018
Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort
由 XZ 提供Jul 31, 2018
The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.
提供方
纽约大学坦登工程学院
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
关于 Machine Learning and Reinforcement Learning in Finance 专项课程
The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.
The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:
(1) mapping the problem on a general landscape of available ML methods,
(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and
(3) successfully implementing a solution, and assessing its performance.
The specialization 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.
The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.
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