CODE 114944 ACADEMIC YEAR 2024/2025 CREDITS 6 cfu anno 2 MATEMATICA 9011 (LM-40) - GENOVA 6 cfu anno 3 STATISTICA MATEM. E TRATTAM. INFORMATICO DEI DATI 8766 (L-35) - GENOVA 6 cfu anno 1 MATEMATICA 9011 (LM-40) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/09 LANGUAGE Italian (English on demand) TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW At the end of the course, the student will have: a good understanding of the basic notions of machine learning and of the related basic mathematical tools; a comprehension of the basic concepts and techniques of convex optimization a good knowledge of the statistical and computational properties of some well known machine learning algorithms; Thanks to the lab sessions, at the end, the student will have some ability to implement machine learning algorithms on synthetic and real data sets. AIMS AND CONTENT LEARNING OUTCOMES Provide the tools for theoretical understanding and practical use of the main supervised and unsupervised learning algorithms. AIMS AND LEARNING OUTCOMES At the end of the course, the student will have: a good understanding of the basic notions of machine learning and of the related basic mathematical tools; a comprehension of the basic concepts and techniques of convex optimization a good knowledge of the statistical and computational properties of some well known machine learning algorithms; Thanks to the lab sessions, at the end of the course, the student will have: some ability to implement machine learning algorithms on synthetic and real data sets. TEACHING METHODS Classes using blackboard to introduce the theorethical concepts and the main statistical learning algorithms. Lab activities in parallel to experience how the proposed methods work in practce. SYLLABUS/CONTENT The teaching will offer an introduction to the main tools which are necessary to understand statistical learning, and a number of supervised learning algorithms, such as local methods, regularization networks, linear and non linear models. The Course will also give a basic introduction to neural networks. Unsupervised problems such as clustering and dimensionality reduction will also be treated. All the methods covered in the course will be implemented and during the lab sessions. The teaching will contribute to the following objectives and goals for the Agenda 2030 for sustainable development: Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all Goal 5. Achieve gender equality and empower all women and girls RECOMMENDED READING/BIBLIOGRAPHY L. Rosasco, Introductory Machine Learning Notes, University of Genoa, (http://lcsl.mit.edu/courses/ml/1718/MLNotes.pdf) Steinwart, Ingo, Christmann, Andreas, Support vector machines, Springer, ISBN 978-0-387-77241-7 Cucker, Felipe, Zhou, Ding-Xuan, Learning theory: an approximation theory viewpoint, Cambridge University Press 2007, ISBN 978-0-521-86559-3 Hastie, Tibshirani and Friedman. Elements of statistical learning Shalev-Shwartz and Ben-David. Understanding Machine Learning: from Theory to Algorithms Boyd, Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN 0 521 83378 7 TEACHERS AND EXAM BOARD SILVIA VILLA Ricevimento: By appointment wich can be fixed in person or via email : silvia.villa@unige.it CESARE MOLINARI Exam Board SILVIA VILLA (President) ERNESTO DE VITO CESARE MOLINARI (President Substitute) LESSONS LESSONS START In agreement with the official academic calendar Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION o pass the exam the student has two options: 1) deliver reports on each lab and participate to intermediate written and lab verifications. At the end of the teaching, the student may decide to take an oral exam to improve her/his mark. 2) participate to an oral exam at the end of the teaching on the entire content of the course, after the delivery of the labs' reports. Students with DSA certification ("specific learning disabilities"), disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and examination methods that, in compliance with the teaching objectives, take account of individual learning arrangements and provide appropriate compensatory tools. ASSESSMENT METHODS The written and the oral exam contain exercises and theoretical questions on the topics covered by the teaching, and will require the comprehension and the ability to use the introduced concepts and algorithms. The lab exam will be a series of reports based on the guided implementation and use of the algorithms introduced in theoretical classes (notebooks will be used). Exam schedule Data appello Orario Luogo Degree type Note 14/02/2025 09:00 GENOVA Esame su appuntamento 19/09/2025 09:00 GENOVA Esame su appuntamento