FIN-407 / 6 crédits

Enseignant: Gourier Elise Marie

Langue: Anglais


Summary

This course aims to give an introduction to the application of machine learning to finance. These techniques gained popularity due to the limitations of traditional financial econometrics methods tackling big data. We will review and compare traditional methods and machine learning algorithms.

Content

Keywords

Machine Learning, Deep Learning, NLP

Learning Prerequisites

Required courses

 Econometrics

 

Recommended courses

Introduction to finance

Important concepts to start the course

Basic linear algebra.

Basic probabilistic and statistical concepts.

Learning Outcomes

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

  • Elaborate a machine learning algorithm
  • Assess / Evaluate the performance of different models
  • Formulate hypotheses behind different models
  • Propose optimal methods for problems seen
  • Optimize techniques / algorithms used
  • Construct a parsimonious model
  • Implement machine learning algorithms
  • Exploit information contained in data

Transversal skills

  • Give feedback (critique) in an appropriate fashion.
  • Demonstrate the capacity for critical thinking
  • Use a work methodology appropriate to the task.

Teaching methods

Lectures and exercise sessions

Projects

Expected student activities

  • Participate in lectures
  • Participate in exercises sessions
  • Solve the problem sets
  • Work on a project and present outcomes
  • Write a final exam

Assessment methods

(Project report+Project presentation+Exam)/3

Supervision

Assistants Yes

Resources

Bibliography

Dixon M. F, Halperin I. and Bilokon P. (2020): "Machine Learning in Finance", Springer

 

Ressources en bibliothèque

Moodle Link

Prerequisite for

  • Courses using statistical dynamic models

Dans les plans d'études

  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning in finance
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning in finance
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning in finance
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10  ODY016  
10-11    
11-12    
12-13     
13-14  EXTRANEF126  
14-15    
15-16    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Mercredi, 9h - 12h: Cours ODY016

Mercredi, 13h - 16h: Exercice, TP EXTRANEF126

Cours connexes

Résultats de graphsearch.epfl.ch.