OVERVIEW

The goal of this course is to provide an overview of classical Machine Learning algorithms, discussing modeling and computational aspects.

AIMS AND CONTENT

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.

TEACHERS AND EXAM BOARD

Exam Board

NICOLETTA NOCETI (President)

ELENA NICORA

LORENZO ROSASCO (President Substitute)

ALESSANDRO VERRI (Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

• 40% continuous assessment
• 20% project (in groups)
• 40% theory oral  

ASSESSMENT METHODS

• timely delivery of assignments
• active participation in class
• final project on a method or use-case, and presentation of the obtained results in a seminar
• oral exam

Exam schedule