Data mining

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

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