Machine Learning : empirical applications for finance (prerequisite : Python for Finance) (This course corresponds to the bloc 2/3 of the Certificate "Fundamentals of Data Science")

Crédit : 3 ECTS
Langue du cours : anglais

Volume horaire

• CM : 21 h

Compétences à acquérir

Building Machine Learning (ML) models for Finance problems. Using ML Python library (and in particular sickit-learn).

Description du contenu de l'enseignement

Basics of ML

• Definitions, approaches and applications.
• Data mining (DM) : definitions and links with ML.
• Classification and regression problems.
• Building and evaluating an ML model.
• Presentation of the main approaches of ML/DM.
• Application I.

Decision Trees :

• Definitions and algorithms.
• Advanced methods based on DL : Bagging, Boostring and Random forests.
• Application II : Making a decision in finance.

Neural networks:

• Definitions.
• Learning in NN : grandient descent and Backpropagation.
• Advanced methods based on NN (Deep learning).
• Application III : : Stock pricing.

Reinforcement Learning :

• Definitions : Agents and environnments.
• Markovian Decision Process (MDP).
• Policies and optimal policies.
• Q-learning.
• Application IV : Trading.

Mode de contrôle des connaissances

Two/Three assignments (building a model + Python programming).

Pré-requis obligatoires

Python programming language.

Enseignant responsable

HOUCINE SENOUSSI