UMontrealX: Machine Learning Use Cases in Finance

UMontrealX: Machine Learning Use Cases in Finance

by Université de Montréal

Deep Learning in Quantitative Finance

Course Description

"Deep Learning in Quantitative Finance" is an innovative and comprehensive course that bridges the gap between cutting-edge machine learning techniques and their practical applications in the financial sector. This course, developed by IVADO and Fin-ML, offers a unique opportunity to explore how deep learning and advanced analytics are revolutionizing financial, banking, and insurance industries.

What Students Will Learn

  • Recognize appropriate use cases for machine learning models in financial contexts
  • Apply best practices of machine learning and deep learning in finance
  • Identify and understand specialized deep network architectures for solving financial problems, including:
    • Graph neural networks in financial markets
    • Reinforcement learning for portfolio optimization
    • Information extraction techniques for ESG metrics

Prerequisites

  • Intermediate knowledge of mathematics
  • Programming skills (preferably in Python)
  • Basic understanding of machine learning concepts (helpful but not required)

Course Content

  • Review of machine learning and deep learning applications in finance
  • Neural network architectures on graphs for financial markets and bitcoin transactions
  • Portfolio design using reinforcement learning
  • Natural Language Processing for information extraction from financial disclosures
  • ESG and sustainable finance applications
  • Real-world examples and case studies from industry experts

Target Audience

  • Industry professionals in finance, banking, and insurance
  • Academics interested in applied machine learning
  • Graduate students in data science and quantitative finance
  • Anyone interested in AI applications in the financial sector

Real-World Applications

  • Enhance financial market analysis using graph neural networks
  • Optimize investment portfolios with reinforcement learning techniques
  • Extract valuable information from financial disclosures for ESG evaluation
  • Implement advanced machine learning models in various financial applications
  • Stay competitive in the rapidly evolving field of quantitative finance

Syllabus

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

This course offers a unique blend of theoretical knowledge and practical applications, taught by six international experts from both academia and industry. By enrolling, you'll gain invaluable insights into the cutting-edge technologies shaping the future of finance, positioning yourself at the forefront of this rapidly evolving field.

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