Machine Learning 1

Presentation

The course introduces machine learning through a global presentation of the field, before dealing more precisely with theoretical aspects of statistical learning and several algorithms. The practical work presents situations where the implementation of methods requires an understanding of the theory associated with them.

Learning outcomes

• Understand the theoretical foundations of the machine learning methods presented.
• Implement these methods appropriately, without considering them as black boxes.
• Make the link between the different methods.

Syllabus

The course presents

• a broad view of the field of machine learning,
• more details on the concepts of risk
• preanalysis and data preparation
• support vector machines
• decision trees
• boosting and bagging
• vector quantization.