Ects : 5

Enseignant responsable :

• PIERRE WOLINSKI

Volume horaire : 36

Description du contenu de l'enseignement :

Project: work by groups of two, build or use an existing (real) dataset, process the dataset by using the methods presented in the courses.

Implement the different methods presented in the Machine Learning course by using the libraries NumPy, Pandas and scikit-learn on real or synthetic data:

• k-nearest neighbors (k-NNs);
• Linear/Quadratic Discriminant Analysis (LDA/QDA);
• Logistic regression;
• Clustering: k-means, agglomerative clustering;
• Decision tree learning, random forests;
• Neural networks.

Pré-requis obligatoires :

Programming language: Python.

Compétence à acquérir :

Implement common Machine Learning techniques in Python (with the help of the libraries NumPy, Pandas and scikit learn).

Mode de contrôle des connaissances :

Graded tutorial (TP noté) + Project.