关于此课程： 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.
此课程适用人群： 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
制作方： New York University Tandon School of Engineering
教学方： Igor Halperin
| 基本信息 | |
| 级别 | Beginner |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
Artificial Intelligence & Machine Learning
11 个视频, 3 个阅读材料
- 视频: Specialization Objectives
- 视频: Specialization Prerequisites
- 视频: Artificial Intelligence and Machine Learning, Part I
- 视频: Artificial Intelligence and Machine Learning, Part II
- 视频: Machine Learning as a Foundation of Artificial Intelligence, Part I
- 视频: Machine Learning as a Foundation of Artificial Intelligence, Part II
- 视频: Machine Learning as a Foundation of Artificial Intelligence, Part III
- 视频: Machine Learning in Finance vs Machine Learning in Tech, Part I
- 视频: Machine Learning in Finance vs Machine Learning in Tech, Part II
- 视频: Machine Learning in Finance vs Machine Learning in Tech, Part III
- Reading: The Business of Artificial Intelligence
- Reading: How AI and Automation Will Shape Finance in the Future
- Reading: A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1
- Notebook: Euclidean Distance Calculation
已评分: Module 1 Quiz
已评分: Euclidean Distance Calculation
第 2 周
Mathematical Foundations of Machine Learning
6 个视频, 2 个阅读材料
- 视频: The No Free Lunch Theorem
- 视频: Overfitting and Model Capacity
- 视频: Linear Regression
- 视频: Regularization, Validation Set, and Hyper-parameters
- 视频: Overview of the Supervised Machine Learning in Finance
- Reading: I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
- Reading: Leo Breiman, “Statistical Modeling: The Two Cultures”
- Notebook: Linear Regression
已评分: Module 2 Quiz
已评分: Linear Regression
第 3 周
Introduction to Supervised Learning
7 个视频, 3 个阅读材料
- 视频: A First Demo of TensorFlow
- 视频: Linear Regression in TensorFlow
- 视频: Neural Networks
- 视频: Gradient Descent Optimization
- 视频: Gradient Descent for Neural Networks
- 视频: Stochastic Gradient Descent
- Reading: A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)
- Reading: 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.
- Reading: 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. 1-41
- Notebook: Tobit Regression
已评分: Module 3 Quiz
已评分: Tobit Regression
第 4 周
Supervised Learning in Finance
9 个视频, 2 个阅读材料
- 视频: Fundamental Analysis
- 视频: Machine Learning as Model Estimation
- 视频: Maximum Likelihood Estimation
- 视频: Probabilistic Classification Models
- 视频: Logistic Regression for Modeling Bank Failures, Part I
- 视频: Logistic Regression for Modeling Bank Failures, Part II
- 视频: Logistic Regression for Modeling Bank Failures, Part III
- 视频: Supervised Learning: Conclusion
- Reading: C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3
- Reading: A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)
- Notebook: Course Project
已评分: Module 4 Quiz
已评分: Course Project
常见问题解答
运作方式
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制作方
New York University Tandon School of Engineering
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
价格
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| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
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