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RSM2328H – Machine Learning and Financial Innovation (Term 4)

Home » RSM2328H – Machine Learning and Financial Innovation (Term 4)

General Information

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Target Audience


Machine learning is an important branch of artificial intelligence where a computer learns from large volumes of data. Many activities within financial services (as well as other industries) are being impacted by machine learning.  For example, lending decisions, investment strategies, fraud detection, the marketing of financial products, and even hiring decisions now involve machine lending.  If you are hoping to get a job in finance, you should seriously consider taking this course. Machine learning and artificial intelligence are likely to have a big impact on your career. From past experience we know that many MBA internships involve machine learning applications.

Format


12 online sessions over 6 weeks

Course Mission


Students will understand enough about machine learning to be able to work with data science specialists. This is likely to be an essential skill for finance professionals in the future. The course will cover the main techniques used by data scientists to handle large data sets for prediction, clustering, and interacting with a changing environment.

Python is currently the language of choice for machine learning and is now widely used in business. As many people have pointed out, “Python is the new Excel.” It is becoming impossible to get many jobs in finance without having Python on your resume. Students should make every effort to develop some Python skills before taking this elective.  The Python for Business course offered by Rotman is one way they can do this.

Course Scope


The course will introduce students to the tools of machine learning and allow them to become comfortable with the way Python is used for machine learning projects. They will undertake some assignments on their own and larger projects in groups. There will be group presentations involving a variety of innovations that are changing the financial sector.

Evaluation and Grade Breakdown

ComponentWeight
Class Participation10%
Individual Assignments20%
Group Projects20%
Class Presentations10%
Final Exam (During Final Exam Period)40%

Required Resources


“Machine learning in Business: An Introduction to the World of Data Science” 3rd edition, 2021, John Hull.


This page was last updated: 2024-03-01 @ 11:02 am