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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
67008 STRASBOURG
0368851672

Responsable

Nicolas Lachiche

Gilles Marcou


MASTER - Chimie