CODE 108717 ACADEMIC YEAR 2024/2025 CREDITS 10 cfu anno 2 INGEGNERIA ELETTRONICA 8732 (LM-29) - GENOVA TEACHING LOCATION GENOVA MODULES Questo insegnamento è composto da: DEEP LEARNING MACHINE LEARNING FOR AUTOMATED DRIVING TEACHING MATERIALS AULAWEB 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 NICOLETTA NOCETI Ricevimento: Please contact the instructor by email of preferably via Teams. VITTORIO MURINO VITO PAOLO PASTORE FRANCESCO BELLOTTI Ricevimento: On appointment: mail (francesco.bellotti@unige.it) or on Teams or after lecture 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.
