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.
本課程是 Machine Learning and Reinforcement Learning in Finance 專項課程 專項課程的一部分
Guided Tour of Machine Learning in Finance
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課程信息
38,362 次近期查看
Learner Career Outcomes
50%
完成這些課程後已開始新的職業生涯
47%
通過此課程獲得實實在在的工作福利
Learner Career Outcomes
50%
完成這些課程後已開始新的職業生涯
47%
通過此課程獲得實實在在的工作福利
100% 在線
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中級
完成時間大約為21 小時
建議：11 hours/week
英語（English）
字幕：英語（English）
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 3 小時
Artificial Intelligence & Machine Learning
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
2完成時間為 6 小時
Mathematical Foundations of Machine Learning
6 個視頻 （總計 45 分鐘）, 3 個閱讀材料, 2 個測驗
6 個視頻
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
3完成時間為 6 小時
Introduction to Supervised Learning
7 個視頻 （總計 75 分鐘）, 4 個閱讀材料, 2 個測驗
7 個視頻
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
4完成時間為 10 小時
Supervised Learning in Finance
9 個視頻 （總計 66 分鐘）, 4 個閱讀材料, 3 個測驗
9 個視頻
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
Jupyter Notebook FAQ10分鐘
1 個練習
Module 4 Quiz21分鐘
3.8
133 條評論來自Guided Tour of Machine Learning in Finance的熱門評論
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.
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
關於 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.
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.
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