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.
This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization
Guided Tour of Machine Learning in Finance
Offered By
Machine Learning and Reinforcement Learning in Finance SpecializationNew York University Tandon School of Engineering
Syllabus - What you will learn from this course
Week
13 hours to complete
Artificial Intelligence & Machine Learning
11 videos (Total 75 min), 3 readings, 1 quiz
11 videos
Specialization Objectives8m
Specialization Prerequisites7m
Artificial Intelligence and Machine Learning, Part I6m
Artificial Intelligence and Machine Learning, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part I5m
Machine Learning as a Foundation of Artificial Intelligence, Part II7m
Machine Learning as a Foundation of Artificial Intelligence, Part III7m
Machine Learning in Finance vs Machine Learning in Tech, Part I6m
Machine Learning in Finance vs Machine Learning in Tech, Part II6m
Machine Learning in Finance vs Machine Learning in Tech, Part III8m
3 readings
The Business of Artificial Intelligence30m
How AI and Automation Will Shape Finance in the Future30m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 130m
1 practice exercise
Module 1 Quiz30m
Week
26 hours to complete
Mathematical Foundations of Machine Learning
6 videos (Total 45 min), 3 readings, 2 quizzes
6 videos
The No Free Lunch Theorem7m
Overfitting and Model Capacity8m
Linear Regression7m
Regularization, Validation Set, and Hyper-parameters10m
Overview 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, 5.41h
Leo Breiman, “Statistical Modeling: The Two Cultures”1h
Jupyter Notebook FAQ10m
1 practice exercise
Module 2 Quiz15m
Week
36 hours to complete
Introduction to Supervised Learning
7 videos (Total 75 min), 4 readings, 2 quizzes
7 videos
A First Demo of TensorFlow11m
Linear Regression in TensorFlow10m
Neural Networks11m
Gradient Descent Optimization10m
Gradient Descent for Neural Networks12m
Stochastic Gradient Descent8m
4 readings
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)1h
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.15m
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. 1-4115m
Jupyter Notebook FAQ10m
1 practice exercise
Module 3 Quiz15m
Week
410 hours to complete
Supervised Learning in Finance
9 videos (Total 66 min), 4 readings, 3 quizzes
9 videos
Fundamental Analysis7m
Machine Learning as Model Estimation8m
Maximum Likelihood Estimation10m
Probabilistic Classification Models6m
Logistic Regression for Modeling Bank Failures, Part I8m
Logistic Regression for Modeling Bank Failures, Part II5m
Logistic Regression for Modeling Bank Failures, Part III8m
Supervised Learning: Conclusion2m
4 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.31h
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)1h
Jupyter Notebook FAQ10m
Jupyter Notebook FAQ10m
1 practice exercise
Module 4 Quiz21m
3.7
103 Reviews55%
started a new career after completing these courses
53%
got a tangible career benefit from this course
10%
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Top reviews from Guided Tour of Machine Learning in Finance
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.
About New York University Tandon School of Engineering
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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