Description

Machine learning and knowledge discovery from databases.

• Understand machine learning
• Overview of algorithms for clustering, classification, and association rule learning and focus on data representation
• Practice with WEKA and KNIME softwares

• Data pre-processing ; evaluation ; integration ; representations.
• Frequent patterns and association rules.
• Clustering : k means ; expectation maximization.
• Classification : k nearest neighbours ; naive Bayesian classifier.
• Decision trees : principle, classification, regression, sensitivity, random forest.
• Neural networks : single and multiple layers ; backpropagation ; strengths and limits ; example (clustering of reactions by Kohonen maps).
• Support Vector Machinees : principle, classification and regression.
• Genetic algorithms : concepts ; fitness function ; crossover and mutations.
• Labs with WEKA and KNIME.
• Detailed examples

Compétences visées

• Understand challenges and limits of machine learning
• Choose relevant algorithms to cluster, classify or extract association rules from data
• Application of those methods with WEKA and KNIME software

Contacts

Responsable(s) de l'enseignement

• Nicolas Lachiche
• Gilles Marcou

MCC

Les épreuves indiquées respectent et appliquent le règlement de votre formation, disponible dans l'onglet Documents de la description de la formation.

Régime d'évaluation

CT (Contrôle terminal, mêlé de contrôle continu)

Coefficient

3.0

Évaluation initiale / Session principale - Épreuves

Seconde chance / Session de rattrapage - Épreuves