本課程是 Machine Learning and Reinforcement Learning in Finance 專項課程專項課程的一部分

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644 個評分

Igor Halperin

30,118 人已註冊

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Machine Learning and Reinforcement Learning in Finance 專項課程New York University

課程信息

15,050 次近期查看

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.

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第 1 門課程（共 4 門）

Machine Learning and Reinforcement Learning in Finance 專項課程

中級

完成時間大約為24 小時

英語（English）

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 1 門課程（共 4 門）

Machine Learning and Reinforcement Learning in Finance 專項課程

中級

完成時間大約為24 小時

英語（English）

授課教師

授課教師評分

3.89/5 （69 個評分）

Igor Halperin

45,244 個學生

4 門課程

提供方

New York University

授課大綱 - 您將從這門課程中學到什麼

內容評分83%（1,743 個評分）

週

週 1

完成時間為 3 小時

Artificial Intelligence & Machine Learning

完成時間為 3 小時

11 個視頻（總計 75 分鐘）, 3 個閱讀材料, 1 個測驗

11 個視頻

Welcome Note4分鐘

Specialization Objectives8分鐘

Specialization Prerequisites7分鐘

Artificial Intelligence and Machine Learning, Part I6分鐘

Artificial Intelligence and Machine Learning, Part II7分鐘

Machine Learning as a Foundation of Artificial Intelligence, Part I5分鐘

Machine Learning as a Foundation of Artificial Intelligence, Part II7分鐘

Machine Learning as a Foundation of Artificial Intelligence, Part III7分鐘

Machine Learning in Finance vs Machine Learning in Tech, Part I6分鐘

Machine Learning in Finance vs Machine Learning in Tech, Part II6分鐘

Machine Learning in Finance vs Machine Learning in Tech, Part III8分鐘

3 個閱讀材料

The Business of Artificial Intelligence30分鐘

How AI and Automation Will Shape Finance in the Future30分鐘

A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130分鐘

1 個練習

Module 1 Quiz30分鐘

週

週 2

完成時間為 6 小時

Mathematical Foundations of Machine Learning

完成時間為 6 小時

6 個視頻（總計 45 分鐘）, 3 個閱讀材料, 2 個測驗

6 個視頻

Generalization and a Bias-Variance Tradeoff6分鐘

The No Free Lunch Theorem7分鐘

Overfitting and Model Capacity8分鐘

Linear Regression7分鐘

Regularization, Validation Set, and Hyper-parameters10分鐘

Overview of the Supervised Machine Learning in Finance3分鐘

3 個閱讀材料

I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41小時

Leo Breiman, “Statistical Modeling: The Two Cultures”1小時

Jupyter Notebook FAQ10分鐘

1 個練習

Module 2 Quiz15分鐘

週

週 3

完成時間為 6 小時

Introduction to Supervised Learning

完成時間為 6 小時

7 個視頻（總計 75 分鐘）, 4 個閱讀材料, 2 個測驗

7 個視頻

DataFlow and TensorFlow10分鐘

A First Demo of TensorFlow11分鐘

Linear Regression in TensorFlow10分鐘

Neural Networks11分鐘

Gradient Descent Optimization10分鐘

Gradient Descent for Neural Networks12分鐘

Stochastic Gradient Descent8分鐘

4 個閱讀材料

A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1小時

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分鐘

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分鐘

Jupyter Notebook FAQ10分鐘

1 個練習

Module 3 Quiz15分鐘

週

週 4

完成時間為 10 小時

Supervised Learning in Finance

完成時間為 10 小時

9 個視頻（總計 66 分鐘）, 4 個閱讀材料, 3 個測驗

9 個視頻

Regression and Equity Analysis7分鐘

Fundamental Analysis7分鐘

Machine Learning as Model Estimation8分鐘

Maximum Likelihood Estimation10分鐘

Probabilistic Classification Models6分鐘

Logistic Regression for Modeling Bank Failures, Part I8分鐘

Logistic Regression for Modeling Bank Failures, Part II5分鐘

Logistic Regression for Modeling Bank Failures, Part III8分鐘

Supervised Learning: Conclusion2分鐘

4 個閱讀材料

C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31小時

A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1小時

Jupyter Notebook FAQ10分鐘

1 個練習

Module 4 Quiz21分鐘

審閱

3.8

212 條評論

5 stars
41.77%
4 stars
23.91%
3 stars
13.66%
2 stars
10.71%
1 star
9.93%

來自GUIDED TOUR OF MACHINE LEARNING IN FINANCE的熱門評論

由 CL 提供2018年7月11日

This will be a 5 star course when all of the technical issues are resolved. More timely feedback from the staff is desirable as well.

由 CO 提供2019年4月18日

Very Difficult - Impossible to succeed without very strong prior experience. Would deserve more guidelines

由 DW 提供2019年9月8日

Leans heavily on explaining differences between tech and finance applications of ML, but still great!

由 VB 提供2018年8月25日

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.

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關於 Machine Learning and Reinforcement Learning in Finance 專項課程

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.

Machine Learning and Reinforcement Learning in Finance

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Guided Tour of Machine Learning in Finance

課程信息