FIN-423 / 3 credits

Teacher: Ackerer Damien Edouard

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Remark: MA3 only


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

Office hours Yes
Assistants Yes
Forum Yes

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Financial machine learning projects
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Financial machine learning projects
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22