Data mining
- Cours (CM) -
- Cours intégrés (CI) 24h
- Travaux dirigés (TD) -
- Travaux pratiques (TP) -
- Travail étudiant (TE) -
Langue de l'enseignement : Anglais
Description du contenu de l'enseignement
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 à acquérir
- 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
Contact
Faculté de chimie
1, rue Blaise Pascal - BP 2029667008 STRASBOURG CEDEX
0368851672
Formulaire de contact
Responsable
Nicolas Lachiche
Gilles Marcou
