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Machine Learning for Financial Applications
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Machine Learning for Financial Applications

Learn to implement machine learning models in finance, from portfolio optimization to ESG analysis, with industry-leading experts.

Course Cost

₹ 16,113

Beginner

Skill Level

4 Weeks

Self-paced lessons

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.

Manuel Morales

Manuel Morales

Rheia Khalaf

Rheia Khalaf

English

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English

Manuel Morales

Manuel Morales

Rheia Khalaf

Rheia Khalaf

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

Machine Learning
Financial Analytics
Deep Learning
Neural Networks
Portfolio Optimization
ESG Analysis
Python Programming
Quantitative Finance

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

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Manuel Morales
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Course

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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.
Rheia Khalaf
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Course

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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
Alexandre Nguyen
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Course

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Alexandre Nguyen serves as an Instructor at the Fin-ML Network, bringing expertise in machine learning and deep learning applications for financial services. His career combines academic research with practical industry experience, particularly in institutional portfolio management and banking analytics. After completing his Master's degree in econometrics from Université de Montréal, he conducted research at Mila - Quebec AI Institute. His work focuses on developing and implementing advanced analytical solutions for both private and public sector clients, with particular emphasis on portfolio optimization and financial modeling. As a data science consultant, he specializes in applying classical machine learning techniques and deep learning methodologies to solve complex financial challenges. His teaching contributions help bridge the gap between theoretical machine learning concepts and their practical applications in finance.
Frederik Wenkel
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Course

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Frederik Wenkel serves as a PhD candidate in Applied Mathematics at Université de Montréal and Mila - Quebec AI Institute, where he specializes in geometric deep learning and graph neural networks. His research focuses on developing innovative approaches to overcome limitations in graph convolutional networks, particularly addressing the oversmoothness problem in graph-based deep learning. Building on his strong mathematical foundation from Technical University of Munich, where he completed both his bachelor's and master's degrees, he explores applications of these techniques across diverse domains including social networks, biochemistry, and finance. His work combines geometric scattering transforms with residual convolutions to improve node discrimination while maintaining structural awareness in graph data. Through his research, he contributes to advancing the field of geometric deep learning, particularly in addressing challenges where traditional deep learning methods face limitations with non-Euclidean data structures.
Elham Kheradmand
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Elham Kheradmand serves as a Postdoctoral Fellow at Université de Montréal, focusing on developing machine learning methods for sustainable finance and climate change solutions. After completing her PhD in Mathematics from Polytechnique Montréal in 2020, she has combined her expertise in algorithm development and optimization with practical industry experience gained as former head of banking software development at Saman Bank. As an AI project manager at AI Launch Lab and former lead AI scientist at Clearsum, she guides students and professionals in implementing AI-based projects. Her research contributions earned her recognition as a Women in Artificial Intelligence North America 2022 award winner. Beyond her technical work, she actively promotes diversity in technology through mentoring roles at Mila and Athena Pathways, while maintaining memberships in Women in AI and Women in Climate Tech organizations. Her current research focuses on applying artificial intelligence for social good, particularly in developing frameworks for ESG information extraction and sustainable investment decisions
Marie-Ève Malette
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Marie-Ève Malette serves as Director of Development and Partnerships at JACOBB - Centre d'intelligence artificielle appliquée, bringing eight years of expertise in artificial intelligence and financial technology. Her educational background includes a master's degree in Financial Engineering from HEC Montréal and a master's degree in Artificial Intelligence from Université de Montréal, complemented by the CFA designation. Her career spans both banking and finance sectors, where she has led the development of transformative AI tools and methodologies. As a bridge between technical and business domains, she excels in communicating complex AI concepts to executives and policymakers, facilitating the implementation of high-value AI solutions. Her work focuses on helping organizations navigate the practical challenges of AI implementation while ensuring solutions align with business objectives and industry requirements
Machine Learning for Financial Applications

Beginner

Skill Level

4 Weeks

Self-paced lessons

Course Cost

₹ 16,113

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.