CODE 102299 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 3 STATISTICA MATEM. E TRATTAM. INFORMATICO DEI DATI 8766 (L-35) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR SECS-S/01 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW The amount of data in industrial applications is exponentially growing. Efficient and sophisticated tools are needed to manage it. This course presents the most recent methods of data processing and content generation. AIMS AND CONTENT LEARNING OUTCOMES Develop the basic skills for extracting knowledge and knowledge from large data sets, in particular by forming an understanding of the value of data mining in solving real-world problems understanding of foundational concepts underlying data mining understanding of algorithms commonly used in data mining tools ability to apply data mining tools to real-world problems AIMS AND LEARNING OUTCOMES The student will be able manage a data analysis problem in a group, generate a report and thus understand a business phenomenon. PREREQUISITES Coding (Matlab/Python/R), linear algebra, probability and statistics, Machine Learning TEACHING METHODS In-class lectures and laboratory sessions with exercises in Matlab/Python/R SYLLABUS/CONTENT 1 Introduction to data science in the business framework 2 Classic machine learning methods for classification and regression problems 3 Data-centric approaches to improve real-world datasets 4 Methods of optimal selection of the features 5 Neural networks and deep learning 6 Convolutional Networks (CNN) 7 Applications to computer vision 8 Definition of Predictive AI and Generative AI 9 Predictive AI: from feature engineering to the definition of Embedding. Projection into the space of embeddings, clustering and classification algorithms 10 Introduction to Recurrent Neural Networks (RNN) 11 History and development of Generative AI 12 Introduction to GANs (Generative Adversarial Networks) 13 The concept of Transformer and (Self-)Attention 14 Applications of Generative AI 15 Creative text generation 16 Image generation and generative art 17 Applications in medicine, finance, and other industries RECOMMENDED READING/BIBLIOGRAPHY "Deep Learning" by Ian Goodfellow, Yoshua Bengio, Aaron Courville. "Generative Deep Learning" by David Foster. "Probabilistic Machine Learning" by K,P.Murphy (Volume 1) "An Introduction to Statistical Learning with Applications in Python", by G.James et al. Scientific papers and online resources for selected applications TEACHERS AND EXAM BOARD FRANCESCO PORRO LESSONS LESSONS START According to official academic calendar Class schedule The timetable for this course is available here: Portale EasyAcademy FURTHER INFORMATION Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison.