CODE 80249 ACADEMIC YEAR 2026/2027 CREDITS 6 cfu anno 3 INGEGNERIA BIOMEDICA 8713 (L-8) - GENOVA 6 cfu anno 2 INFORMATICA 11896 (L-31 R) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester PREREQUISITES Propedeuticità in ingresso Per sostenere l'esame di questo insegnamento è necessario aver sostenuto i seguenti esami: Computer Science 8759 (coorte 2025/2026) CALCULUS 1 57069 2025 Computer Science 8759 (coorte 2024/2025) CALCULUS 1 57069 2024 Computer Science 11896 (coorte 2025/2026) CALCULUS 1 57069 2025 OVERVIEW The course introduces the fundamental concepts and results of probability theory, which are then applied in the fields of information theory and statistical inference. AIMS AND CONTENT LEARNING OUTCOMES The student will learn how to extract information and infer knowledge from data through the application of basic concepts and techniques from probability theory and statistics AIMS AND LEARNING OUTCOMES At the end of the course, students will have acquired fundamental concepts and results of probability and will be able to apply them to simple problems in information transmission and statistical inference. PREREQUISITES Basic notions of calculus. TEACHING METHODS Traditional. Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles. The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita. SYLLABUS/CONTENT The course introduces the basic concepts of probability theory and then explores the connections with the fundamentals of information theory and inference. About half of the course is covered through examples and exercises, including the implementation of some algorithms. RECOMMENDED READING/BIBLIOGRAPHY Notes prepared by the instructor. TEACHERS AND EXAM BOARD ALESSANDRO VERRI Ricevimento: Appointment by email LORENZO ROSASCO Ricevimento: Appointment by email LESSONS LESSONS START According to the calendar approved by the Degree Program Board: https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION The exam consists of a written test and an optional oral test for those who wish to improve their written exam grade. During the course, some homework exercises will be assigned, similar to the exam tests. Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/8759/studenti-disabilita-dsa ASSESSMENT METHODS The written exam assesses the student's ability to apply the concepts learned in class by solving exercises similar to those covered during the course. FURTHER INFORMATION For further information, please refer to the course’s AulaWeb module or contact the instructor. Students with valid certifications for Specific Learning Disorders (SLD) may request accommodations for exams at least 7 days prior to the exam date by filling out the “accommodation request form” (available via online services at https://modulionline.unige.it/richiesta-adattamenti# no-back), which will be automatically forwarded by the system to the instructor in charge of the course and to the faculty liaison for students with disabilities and SLDs in their School/Department. The student will receive a copy of their request.