Marcello Restelli

Marcello Restelli is Associate Professor of Computer Engineering at DEIB - Dept. of Electronics, Information and Bioengineering of the Politecnico di Milano, where he obtained the Laurea degree in Computer Science Engineering in 2000 and the Ph.D. in Information Engineering in 2004. He is currently teaching the “Machine Learning” and the “Reinforcement Learning” courses and he is a board member of the national Ph.D. programme in Artificial Intelligence - Industry 4.0. His research interests focus on machine learning algorithms and, in particular, the development of reinforcement learning techniques and their application to real-world problems (e.g., robotics, finance, autonomous vehicles, water resource management, etc.). He has published more than 150 peer-reviewed papers on some of the most prestigious international conferences and journals in the machine-learning and robotic fields. He has served as reviewer for several international journals and he has been member of the programme committee of the main international conference of his research area, among which ICML, NIPS, AAAI, and IJCAI. He is principal investigator of several research projects funded both by public entities and by some of the main Italian companies.

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Machine Learning: an overview (Coursera) Coursera
Politecnico di Milano

Machine Learning: an overview (Coursera)

Embark on a journey to understand the basics of Machine Learning with 'Machine Learning: An Overview' - an introductory course designed for beginners. This course will guide you through the taxonomy of machine learning problems, explore key algorithms, and illustrate their applications and constraints using real-world examples and case studies.

Aug 10th 2026
3 Weeks
Machine Learning (POK) Polimi OPEN KNOWLEDGE
Politecnico di Milano

Machine Learning (POK)

Discover the transformative world of Machine Learning with our beginner-friendly MOOC. Explore key methods that are revolutionizing sectors worldwide and learn how these techniques can be applied to solve complex problems. Our course offers a concise yet thorough overview of machine learning fundamentals, including problem taxonomy, algorithmic solutions, and practical case studies.

Available
3 Weeks
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