OVERVIEW

Machine learning for automated driving:
The course aims to explore the theory and application of state-of-the-art techniques in machine learning, particularly on the perception.

AIMS AND CONTENT

LEARNING OUTCOMES

Machine learning for automated driving:
The course aims to provide students with the essential knowledge on machine learning techniques used in automated driving, particularly with regard to context perception and prediction.
The course also intends to introduce students to the development of firmware on microcontroller electronic systems for the execution of some of the machine learning models studied.
The student will develop analytical and design skills through the production of a project.

PREREQUISITES

Digital systems electronics

Fundamentals of programming

Machine learning (First year master's degree)

TEACHERS AND EXAM BOARD

Exam Board

FRANCESCO BELLOTTI (President)

LUCA LAZZARONI

MARCO RAGGIO

RICCARDO BERTA (President Substitute)

EXAMS

EXAM DESCRIPTION

Project work on an example of machine learning application for automated driving (knowledge, understanding, analysis, judgement, application, creation in relation to the topics covered in the lecture)

ASSESSMENT METHODS

Assessment will take place at the various stages of project preparation as set out in the Examination methods: definition interviews, design/implementation of the solution, final discussion of a paper describing the work carried out.

The lecturer will also take into account the student's participation during the course.

Exam schedule