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.
This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization
Offered By
Machine Learning and Reinforcement Learning in Finance SpecializationNew York UniversityAbout this Course
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Learner Career Outcomes
50%
started a new career after completing these courses
47%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Course 1 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish
Instructor
Learner Career Outcomes
50%
started a new career after completing these courses
47%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 24 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish
Offered by
New York University
New York University is a leading global institution for scholarship, teaching, and research. Based in New York City with campuses and sites in 14 additional major cities across the world, NYU embraces diversity among faculty, staff and students to ensure the highest caliber, most inclusive educational experience.
Syllabus - What you will learn from this course
3 hours to complete
Artificial Intelligence & Machine Learning
3 hours to complete
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
6 hours to complete
Mathematical Foundations of Machine Learning
6 hours to complete
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
6 hours to complete
Introduction to Supervised Learning
6 hours to complete
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
10 hours to complete
Supervised Learning in Finance
10 hours to complete
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
Reviews
TOP REVIEWS FROM GUIDED TOUR OF MACHINE LEARNING IN FINANCE
by KDAug 23, 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.
by VBAug 25, 2018
More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.
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 XZJul 31, 2018
The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.
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.
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, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
More questions? Visit the Learner Help Center.
