Financial machine learning projects

Summary

The objective of this course is to acquire experience in financial machine learning by solving real-world problems. Different groups of students will work on different industry projects during the semester. Lectures will discuss best practices and tools.

Content

Keywords

• finance
• machine learning
• projects

Learning Prerequisites

Required courses

• Programming knowledge of Python
• Basic probability and statistical knowledge
• Basic knowledge of finance
• Basic knowledge of machine learning

Recommended courses

• Introduction to finance
• Financial econometric
• Derivatives
• Investments

Learning Outcomes

By the end of the course, the student must be able to:

• Choose an appropriate model to solve a problem in finance
• Assess / Evaluate and benchmark a model performance
• Design flexible models for financial applications
• Implement data processing and models in python code
• Develop a fast system to replace slow numerical methods
• Manipulate and transform data

Transversal skills

• Manage priorities.
• Make an oral presentation.
• Write a scientific or technical report.
• Demonstrate a capacity for creativity.
• Take feedback (critique) and respond in an appropriate manner.
• Continue to work through difficulties or initial failure to find optimal solutions.
• Demonstrate the capacity for critical thinking

Teaching methods

• Lectures, 2 hours per week for 14 weeks
• Project sessions, 1 hour per week for 14 weeks

Expected student activities

• Actively participate to the lectures and the presentations

Assessment methods

• Class participation 20%
• Project presentations 20%
• Project report 60%

Supervision