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

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649 ratings

Igor Halperin

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Machine Learning and Reinforcement Learning in Finance SpecializationNew York University

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Course 1 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Intermediate Level

Approx. 24 hours to complete

English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Intermediate Level

Approx. 24 hours to complete

English

Instructor

Instructor rating

3.92/5 (71 Ratings)

Igor Halperin

45,969 Learners

4 Courses

Offered by

New York University

Syllabus - What you will learn from this course

Content Rating83%(1,750 ratings)

Week1

Week 1

3 hours to complete

Artificial Intelligence & Machine Learning

3 hours to complete

11 videos (Total 75 min), 3 readings, 1 quiz

11 videos

Welcome Note4mSpecialization Objectives8mSpecialization Prerequisites7mArtificial Intelligence and Machine Learning, Part I6mArtificial Intelligence and Machine Learning, Part II7mMachine Learning as a Foundation of Artificial Intelligence, Part I5mMachine Learning as a Foundation of Artificial Intelligence, Part II7mMachine Learning as a Foundation of Artificial Intelligence, Part III7mMachine Learning in Finance vs Machine Learning in Tech, Part I6mMachine Learning in Finance vs Machine Learning in Tech, Part II6mMachine Learning in Finance vs Machine Learning in Tech, Part III8m

3 readings

The Business of Artificial Intelligence30mHow AI and Automation Will Shape Finance in the Future30mA. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130m

1 practice exercise

Module 1 Quiz30m

Week2

Week 2

6 hours to complete

Mathematical Foundations of Machine Learning

6 hours to complete

6 videos (Total 45 min), 3 readings, 2 quizzes

6 videos

Generalization and a Bias-Variance Tradeoff6mThe No Free Lunch Theorem7mOverfitting and Model Capacity8mLinear Regression7mRegularization, Validation Set, and Hyper-parameters10mOverview of the Supervised Machine Learning in Finance3m

3 readings

I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.41hLeo Breiman, “Statistical Modeling: The Two Cultures”1hJupyter Notebook FAQ10m

1 practice exercise

Module 2 Quiz15m

Week3

Week 3

6 hours to complete

Introduction to Supervised Learning

6 hours to complete

7 videos (Total 75 min), 4 readings, 2 quizzes

7 videos

DataFlow and TensorFlow10mA First Demo of TensorFlow11mLinear Regression in TensorFlow10mNeural Networks11mGradient Descent Optimization10mGradient Descent for Neural Networks12mStochastic Gradient Descent8m

4 readings

A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1hE. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15mJ. 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. 1-4115mJupyter Notebook FAQ10m

1 practice exercise

Module 3 Quiz15m

Week4

Week 4

10 hours to complete

Supervised Learning in Finance

10 hours to complete

9 videos (Total 66 min), 4 readings, 3 quizzes

9 videos

Regression and Equity Analysis7mFundamental Analysis7mMachine Learning as Model Estimation8mMaximum Likelihood Estimation10mProbabilistic Classification Models6mLogistic Regression for Modeling Bank Failures, Part I8mLogistic Regression for Modeling Bank Failures, Part II5mLogistic Regression for Modeling Bank Failures, Part III8mSupervised Learning: Conclusion2m

4 readings

C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31hA. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1hJupyter Notebook FAQ10mJupyter Notebook FAQ10m

1 practice exercise

Module 4 Quiz21m

Reviews

3.8

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TOP REVIEWS FROM GUIDED TOUR OF MACHINE LEARNING IN FINANCE

by RSAug 29, 2019

Great course! Relevant concepts are described in the videos and the bibliography is accurate to cover the rest.

by MSDec 31, 2018

Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort

by ITJun 15, 2019

A more detailed introduction and guide to python for machine learning would have made this course one of the best out there

by LPOct 22, 2021

Very useful course. Personally, I think that there should have been more focus on the implementation of tensorflow and neural network codes. Overall the course is well structured and very clear.

View all reviews

About the Machine Learning and Reinforcement Learning in Finance Specialization

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

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Guided Tour of Machine Learning in Finance

About this Course