UMontrealX: Machine Learning Use Cases in Finance
In the last six years, the financial sector has seen an increase in the use of machine learning models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.
There is one session available:
Machine Learning Use Cases in Finance
About this courseSkip About this course
The success of machine learning, and in particular deep learning in image recognition and natural language processing applications, has created high expectations and their use has rapidly spread to many different areas. The financial sector is no exception and the last six years have seen an increase in these types of models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.
In this course, we will first present a review of some of the applications of machine learning and deep learning. We will then illustrate their use in financial applications through concrete examples that we have seen have sparked interest in the industry. Our examples will illustrate how we can add value through ad hoc construction of architectures rather than a simple exercise of replacing classical models with more complex ones, such as multi-layer networks.
We will see
- Neural network architectures on graphs to integrate new information dimensions in financial markets and bitcoin transactions
- Portfolio design using reinforcement learning and
- Natural Language Processing and information extraction methods from financial disclosures in the in an ESG and sustainable finance context
This course was developed by IVADO and Fin-ML as part of a workshop that takes place yearly in Montréal, since 2018. You will be accompanied throughout and given concrete examples by six international experts from both Academia and Industry.
The course is primarily intended for industry professionals and academics with intermediate knowledge of mathematics and programming (ideally Python). Graduate students in data science and quantitative finance (mainly those who are not yet familiar with machine learning and deep learning) may find this content instructive and compelling. The content of this course will also be of great use to whomever uses or is interested in AI, in any other way. Previous experience in the financial industry is not necessary to follow this course.
This course is brought to you by IVADO, Fin-ML and Université de Montréal.
Course created with support from
At a glance
- Language: English
- Video Transcript: English
- Associated skills: Reinforcement Learning, Bitcoin, Deep Learning, Basic Math, Artificial Intelligence, Finance, Computer Vision, Python (Programming Language), Information Extraction, Financial Services, Mathematical Finance, Natural Language Processing, Machine Learning, Data Science, Research, Financial Market
What you'll learnSkip What you'll learn
At the end of the MOOC, participants should be able to:
- Recognize when and how to use machine learning models according to the business context.
- Apply the best practices of machine learning and in particular of deep learning in a financial application context.
- Identify some models and architectures of deep networks that can be used to solve problems in finance and insurance:
- Graph neural networks in financial markets
- Reinforcement learning in portfolio optimization
- Information extraction and ESG metrics
These are the topics of each module:
Module 1 - Introduction and Background
Module 2 - Reminder Machine Learning and Deep Learning
Module 3 - GNN in Finance
Module 4 - ESG Evaluation
Module 5 - Portfolio Design using Reinforcement Learning
Module 6 - Conclusion
Frequently Asked QuestionsSkip Frequently Asked Questions
What is the complete list of speakers for this course?