Machine Learning for Financial Applications
Learn to implement machine learning models in finance, from portfolio optimization to ESG analysis, with industry-leading experts.
Learn to implement machine learning models in finance, from portfolio optimization to ESG analysis, with industry-leading experts.
This comprehensive course explores practical applications of machine learning in finance, banking, and insurance. Developed by IVADO and Fin-ML experts, the curriculum covers advanced topics like neural networks on graphs for financial markets, reinforcement learning for portfolio design, and NLP for ESG analysis. Through real-world examples and industry cases, students learn to implement sophisticated ML models while understanding their practical applications in financial contexts.
Instructors:
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What you'll learn
Understand when and how to apply machine learning models in financial contexts
Master deep learning best practices for financial applications
Implement graph neural networks for financial market analysis
Apply reinforcement learning techniques to portfolio optimization
Utilize NLP methods for ESG metrics and financial disclosure analysis
Develop practical skills in financial machine learning implementation
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course provides a comprehensive overview of machine learning applications in finance, focusing on practical implementation and industry relevance. The curriculum covers essential topics including neural network architectures for financial markets, graph-based analysis of bitcoin transactions, portfolio optimization using reinforcement learning, and natural language processing for ESG analysis. Through concrete examples and real-world applications, students learn how to effectively apply machine learning techniques to solve complex financial problems.
Introduction and Background
Module 1
Reminder Machine Learning and Deep Learning
Module 2
GNN in Finance
Module 3
ESG Evaluation
Module 4
Portfolio Design using Reinforcement Learning
Module 5
Conclusion
Module 6
Fee Structure
Instructors
1 Course
Distinguished AI and Financial Mathematics Expert
Manuel Morales serves as Associate Professor in the Department of Mathematics and Statistics at Université de Montréal while holding the position of Digital Strategy & Innovation Advisor at PSP Investments. As General Director of the FinML network, he leads initiatives to bridge academic research with industry applications in AI-enabled finance. His expertise spans mathematical finance, stochastic processes, and artificial intelligence, with particular focus on sustainable finance and ESG measurement. His experience includes serving as Chief AI Scientist at National Bank of Canada, where he led AI transformation initiatives for a twenty-billion-dollar banking institution. As a researcher affiliated with OBVIA, he contributes to discussions on AI ethics and responsible deployment. His current work focuses on developing machine learning methods for ESG scoring, responsible investment decisions, and training the next generation of finance professionals in AI applications. Through his leadership in multiple collaborative initiatives, he continues to strengthen connections between technical data science teams and product managers while promoting robust AI governance structures in financial institutions.
1 Course
Distinguished Financial AI Expert and Partnership Leader
Rheia Khalaf serves as Director of Partnerships at Mila – Quebec Artificial Intelligence Institute, bringing over fifteen years of expertise in actuarial science, artificial intelligence, and risk management. Her career spans roles at prestigious organizations including IVADO, Fiera Capital, and EY. As a Fellow of the Society of Actuaries (FSA) and Canadian Institute of Actuaries (FCIA), she focuses on bridging academic research with industry applications in AI and finance. Her work encompasses responsible AI implementation, sustainable finance, and climate risk assessment. She holds a B.Sc. in actuarial mathematics from Concordia University and an M.Sc. in risk management from the University of Zurich/ETH Zurich, complemented by a certificate in sustainable investing from Queen's University. As a member of the AMF's technology transfer committee and OBVIA, she contributes to advancing AI governance while mentoring startups and co-authoring publications on artificial intelligence
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