LEARNING OUTCOMES

Learning how to use classical supervised and unsupervised machine learning algorithms by grasping the underlying computational and modeling issues.

AIMS AND LEARNING OUTCOMES

Students will be provided with basic ideas behind statistical learning and a number of prototypical supervised approaches, including, local methods, regularization networks, linear and non linear models. The Course also covers basic unsupervised problems such as clustering and dimensionality reduction. Special effort is devoted to discussing how to set up a reliable machine learning pipeline.

Students will be involved in project activities.

PREREQUISITES

Basic probability, calculus, linear algebra, programming.

TEACHING METHODS

Classes and practical lab sessions. 

SYLLABUS/CONTENT

Course content

• Machine Learning basics
• Empirical risk minimization
• Feature maps and kernels
• Variable selection and dimensionality reduction
• Clustering
• Neural Networks

RECOMMENDED READING/BIBLIOGRAPHY

Material provided by the instructors (slides and papers), see the course Aulaweb page additional references.