Guided Tour of Machine Learning in Finance

Guided Tour of Machine Learning in Finance

This course is part of Machine Learning and Reinforcement Learning in Finance Specialization

Instructor: Igor Halperin

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Assessments

4 assignments

Taught in English

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This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization

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There are 4 modules in this course

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.

What's included

11 videos3 readings1 assignment

11 videosTotal 75 minutes

Welcome Note4 minutesPreview module
Specialization Objectives8 minutes
Specialization Prerequisites7 minutes
Artificial Intelligence and Machine Learning, Part I6 minutes
Artificial Intelligence and Machine Learning, Part II7 minutes
Machine Learning as a Foundation of Artificial Intelligence, Part I5 minutes
Machine Learning as a Foundation of Artificial Intelligence, Part II7 minutes
Machine Learning as a Foundation of Artificial Intelligence, Part III7 minutes
Machine Learning in Finance vs Machine Learning in Tech, Part I6 minutes
Machine Learning in Finance vs Machine Learning in Tech, Part II6 minutes
Machine Learning in Finance vs Machine Learning in Tech, Part III8 minutes

3 readingsTotal 90 minutes

The Business of Artificial Intelligence30 minutes
How AI and Automation Will Shape Finance in the Future30 minutes
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130 minutes

1 assignmentTotal 30 minutes

Module 1 Quiz30 minutes

What's included

6 videos3 readings1 assignment1 programming assignment1 ungraded lab

6 videosTotal 45 minutes

Generalization and a Bias-Variance Tradeoff6 minutesPreview module
The No Free Lunch Theorem7 minutes
Overfitting and Model Capacity8 minutes
Linear Regression7 minutes
Regularization, Validation Set, and Hyper-parameters10 minutes
Overview of the Supervised Machine Learning in Finance3 minutes

3 readingsTotal 130 minutes

I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.460 minutes
Leo Breiman, “Statistical Modeling: The Two Cultures”60 minutes
Jupyter Notebook FAQ10 minutes

1 assignmentTotal 15 minutes

Module 2 Quiz15 minutes

1 programming assignmentTotal 90 minutes

Euclidean Distance Calculation90 minutes

1 ungraded labTotal 60 minutes

Euclidean Distance Calculation60 minutes

What's included

7 videos4 readings1 assignment1 programming assignment1 ungraded lab

7 videosTotal 75 minutes

DataFlow and TensorFlow10 minutesPreview module
A First Demo of TensorFlow11 minutes
Linear Regression in TensorFlow10 minutes
Neural Networks11 minutes
Gradient Descent Optimization10 minutes
Gradient Descent for Neural Networks12 minutes
Stochastic Gradient Descent8 minutes

4 readingsTotal 100 minutes

A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)60 minutes
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 minutes
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 minutes
Jupyter Notebook FAQ10 minutes

1 assignmentTotal 15 minutes

Module 3 Quiz15 minutes

1 programming assignmentTotal 90 minutes

Linear Regression90 minutes

1 ungraded labTotal 60 minutes

Linear Regression60 minutes

What's included

9 videos4 readings1 assignment2 programming assignments2 ungraded labs

9 videosTotal 66 minutes

Regression and Equity Analysis7 minutesPreview module
Fundamental Analysis7 minutes
Machine Learning as Model Estimation8 minutes
Maximum Likelihood Estimation10 minutes
Probabilistic Classification Models6 minutes
Logistic Regression for Modeling Bank Failures, Part I8 minutes
Logistic Regression for Modeling Bank Failures, Part II5 minutes
Logistic Regression for Modeling Bank Failures, Part III8 minutes
Supervised Learning: Conclusion2 minutes

4 readingsTotal 140 minutes

C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.360 minutes
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)60 minutes
Jupyter Notebook FAQ10 minutes
Jupyter Notebook FAQ10 minutes

1 assignmentTotal 21 minutes

Module 4 Quiz21 minutes

2 programming assignmentsTotal 180 minutes

Tobit Regression90 minutes
Course Project90 minutes

2 ungraded labsTotal 180 minutes

Tobit Regression60 minutes
Course Project120 minutes

Instructor

Instructor ratings

4.0 (77 ratings)

Igor Halperin

New York University

4 Courses54,685 learners

Offered by

New York University

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Reviewed on Nov 6, 2019

Fantastic lectures, great first programming assignments with unfortunate tail quality of the programming assignments

Reviewed on Aug 24, 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.

Reviewed on Apr 19, 2019

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

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