Machine Learning : empirical applications for finance (Bloc 3/3 of the Certificate "Fundamentals of Data Science")

ECTS : 3

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. 

Compétence à acquérir :

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

Mode de contrôle des connaissances :

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