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Guided Tour of Machine Learning in Finance
  • Level Professional
  • المدة 24 ساعات hours
  • الطبع بواسطة New York University
  • Offered by

عن

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.

الوحدات

Introduction to the Specialization "Machine Learning and Reinforcement Learning in Finance"

    • 3 Videos
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3 Videos

  • Welcome Note
  • Specialization Objectives
  • Specialization Prerequisites

Artificial Intelligence and Machine Learning

    • 2 Videos
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2 Videos

  • Artificial Intelligence and Machine Learning, Part I
  • Artificial Intelligence and Machine Learning, Part II

Machine Learning as a Foundation of Artificial Intelligence

    • 3 Videos
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3 Videos

  • Machine Learning as a Foundation of Artificial Intelligence, Part I
  • Machine Learning as a Foundation of Artificial Intelligence, Part II
  • Machine Learning as a Foundation of Artificial Intelligence, Part III

Machine Learning in Finance vs Machine Learning in Tech

    • 3 Videos
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3 Videos

  • Machine Learning in Finance vs Machine Learning in Tech, Part I
  • Machine Learning in Finance vs Machine Learning in Tech, Part II
  • Machine Learning in Finance vs Machine Learning in Tech, Part III

Readings

    • 3 Readings
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3 Readings

  • The Business of Artificial Intelligence
  • How AI and Automation Will Shape Finance in the Future
  • A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1

Module 1 Assessment

    • 1 Assignment
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1 Assignment

  • Module 1 Quiz

Generalization and a Bias-Variance Tradeoff

    • 6 Videos
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6 Videos

  • Generalization and a Bias-Variance Tradeoff
  • The No Free Lunch Theorem
  • Overfitting and Model Capacity
  • Linear Regression
  • Regularization, Validation Set, and Hyper-parameters
  • Overview of the Supervised Machine Learning in Finance

Readings

    • 2 Readings
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2 Readings

  • I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
  • Leo Breiman, “Statistical Modeling: The Two Cultures”

Module 2 Assessment

    • 1 Readings
    • 1 Lab
    • 1 Programming
    • 1 Assignment
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1 Programming

  • Euclidean Distance Calculation

1 Assignment

  • Module 2 Quiz

1 Labs

  • Euclidean Distance Calculation

1 Readings

  • Jupyter Notebook FAQ

Introduction to Neural Networks and Tensor Flow

    • 7 Videos
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7 Videos

  • DataFlow and TensorFlow
  • A First Demo of TensorFlow
  • Linear Regression in TensorFlow
  • Neural Networks
  • Gradient Descent Optimization
  • Gradient Descent for Neural Networks
  • Stochastic Gradient Descent

Readings

    • 3 Readings
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3 Readings

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

Module 3 Assessment

    • 1 Readings
    • 1 Lab
    • 1 Programming
    • 1 Assignment
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1 Programming

  • Linear Regression

1 Assignment

  • Module 3 Quiz

1 Labs

  • Linear Regression

1 Readings

  • Jupyter Notebook FAQ

Prediction of Earning per Share (EPS) with Scikit-learn and TensorFlow

    • 2 Videos
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2 Videos

  • Regression and Equity Analysis
  • Fundamental Analysis

Machine Learning with Probabilistic Models (Classification Tasks)

    • 7 Videos
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7 Videos

  • Machine Learning as Model Estimation
  • Maximum Likelihood Estimation
  • Probabilistic Classification Models
  • Logistic Regression for Modeling Bank Failures, Part I
  • Logistic Regression for Modeling Bank Failures, Part II
  • Logistic Regression for Modeling Bank Failures, Part III
  • Supervised Learning: Conclusion

Readings

    • 2 Readings
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2 Readings

  • C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3
  • A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)

Module 4 Assessment

    • 1 Readings
    • 1 Lab
    • 1 Programming
    • 1 Assignment
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1 Programming

  • Tobit Regression

1 Assignment

  • Module 4 Quiz

1 Labs

  • Tobit Regression

1 Readings

  • Jupyter Notebook FAQ

Module 4 Project

    • 1 Readings
    • 1 Lab
    • 1 Programming
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1 Programming

  • Course Project

1 Labs

  • Course Project

1 Readings

  • Jupyter Notebook FAQ

Auto Summary

"Guided Tour of Machine Learning in Finance" is a Coursera course designed for finance professionals, day traders, and students in related fields. Focused on Data Science & AI, it provides an introductory overview of Machine Learning applications in finance, including a capstone project on predicting bank closures. The course requires Python and mathematical knowledge, spans 1440 minutes, and offers a professional-level learning experience with a subscription option.

Igor Halperin
Instructor

Igor Halperin