CODE 114470 ACADEMIC YEAR 2026/2027 CREDITS 6 cfu anno 2 COMPUTER SCIENCE 11964 (LM-18) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 1° Semester OVERVIEW The goal of this course is to provide an overview of Machine Learning algorithms dealing with sequential/dynamic data and agents that can interact with the environment within the reinforcement learning framework. AIMS AND CONTENT LEARNING OUTCOMES Learning how to use sequential and reinforcement learning algorithms by grasping the underlying computational and modeling issues. AIMS AND LEARNING OUTCOMES At the end of the course, students will be able to: UNDERSTAND and use machine learning algorithms and models for dynamic data and agents UNDERSTAND how to effectively set-up machine learning pipelines with dynamic data/agents IMPLEMENT the learning algorithms presented in the course DEVELOP the ability to critically analyze analytical results PREREQUISITES Basic probability, calculus, linear algebra, programming. TEACHING METHODS Theoretical classes might be complemented by practical lab sessions 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 will cover the following topics: Dynamical systems Time series Dynamic mode decomposition Neural netoworks for sequential data Reinforcement Learning Multi-Arm Bandits Markov Decision Processes Prediction and Control Monte Carlo and Temporal Differences Methods RECOMMENDED READING/BIBLIOGRAPHY The material provided by the instructors (notes, papers, books), see the course Aulaweb page additional references. TEACHERS AND EXAM BOARD ALESSANDRO VERRI Ricevimento: Appointment by email LORENZO ROSASCO Ricevimento: Appointment by email Exam Board LORENZO ROSASCO (President) ALESSANDRO VERRI (President) LESSONS LESSONS START 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 will be a project and a discussion of the material presented in the course. Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/10852/studenti-disabilita-dsa ASSESSMENT METHODS The exam will evaluate the overall understanding of course material, the capability to generalize the concepts to unseen problems and analyze the obtained results. Clarity of exposition, completeness of the concepts, quality of the proposed solutions and critical thinking will be taken into account. 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.
