Machine learning

Description

Objectifs

At the end of this module, the student will have understood and be able to explain (main concepts):

- Properties and limits of the main machine learning algorithms.
- Bias - variance trade-off, model selection.
- Algorithms for risk estimation: bootstrap, cross validation.
- Optimization and algorithmic implementations with R and Python (Scikit-learn) of the studied algorithms.
- Ethical and legal concepts of artificial intelligence.

The student will be able to:
- Analyse big data sets from various domains: insurance, marketing, industry, by using R and Python libraries.
- Execute the main machine learning methods and algorithms (discriminant analysis, k-nn, support vector machines, classification and regression trees, random forests, neural networks..)
- Optimize hyper-parameters values and construct pipelines for automating.
- Optimize the missing values management.
- Detect ethical or legal failures (bias, discrimination, opacity) of machine learning algorithms.

Pré-requis

Évaluation

L’évaluation des acquis d’apprentissage est réalisée en continu tout le long du semestre. En fonction des enseignements, elle peut prendre différentes formes : examen écrit, oral, compte-rendu, rapport écrit, évaluation par les pairs…