Course description

Title of the Teaching Unit

Machine Learning for Business

Code of the Teaching Unit

21MQ040

Academic year

2026 - 2027

Cycle

Number of credits

Number of hours

Quarter

Weighting

Site

Anjou

Teaching language

French

Teacher in charge

CUVELIER Etienne

Objectives and contribution to the program

This module covers quantitative techniques for data exploration, interpretation and, above all, automation and prediction, from a practical perspective.
By the end of the course, students will be able to select and apply the quantitative techniques required to analyse a practical problem drawn from the real world of economics.

Prerequisites and corequisites

Data Science for Business

Content

1) Predictive Methods
a) Regression methods
i) Linear (bivariate and multivariate)
ii) Non-linear (bivariate and multivariate)
b) Association rules
c) Classification
i) Introduction
ii) Techniques for validating results
iii) k-nearest neighbours
iv) Bayesian classifiers
(1) Naive Bayes
(2) EM Algorithm for Clustering with MCLUST
v) Decision Trees
vi) Support Vector Machines
vii) Artificial Neural Networks
d) Sentiment Analysis

Teaching methods

- Lectures and practical sessions in the laboratory using specialist software
- Practical case studies

Assessment method

The examination will be oral and will include, amongst other things, the presentation and defence of a group project.

References

- Data Mining and Analysis, Fundamental Concepts and Algorithms, Zaki M. J., Meira, W Jr, Cambridge University Press, May 2014.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Hastie T., Tibshirani R., Friedman J., Springer, 2009
- Social media mining: an introduction, Zafarani R., Abbasi M.A., Liu H, Cambridge University Press, 2014.
- An Introduction to Statistical Learning with Applications in R, James G. , Witten D. , Hastie T., Tibshirani R., Springer, 2009.
- R Programming for Data Science, Peng R. D., LeanPPub, 2016.
N.B.: Toutes les références sont en téléchargement libre légalement.