The course covers the basics of Machine Learning with theoretical and practical applications.

• Introduction to Machine learning
• Discriminant analysis
• Logistic regression
• Support Vector Machines
• Kernel methods

• Elementary illustrations of Discriminant Analysis and Logistic regression
• Detailed lab on Logistic regression
• Elementary illustrations of Support Vector Machines
• Elementary illustrations of Feed Forward Neural Networks

• Bayes classifier and Excess risk
• Discriminant analysis
• Logistic regression
• Support Vector Machines
• Kernel methods
• Feed Forward Neural Networks

• Bayes classifier and Empirical risk minimization