teaching | Marco Piangerelli

Ph.D. courses

Machine Learning and its applications (2 ECTS, 14 hours), Ph.D. programme in Computer Science and Mathematics, University of Camerino. Delivered in English.

2025/2026, also for the Ph.D. programme of national interest (DIN) in Theoretical and Applied Neuroscience
2024/2025, also for the DIN in Theoretical and Applied Neuroscience
2023/2024, also for the DIN in Theoretical and Applied Neuroscience (course page)
2022/2023 (course page)
2021/2022 (course page)
2020/2021 (course page)
2019/2020

Master’s courses

2024/2025: Fundamentals of Machine Learning (6 ECTS), M.Sc. in Computer Science (LM-18), University of Camerino
2023/2024: Fundamentals of Machine Learning (6 ECTS), M.Sc. in Computer Science (LM-18) (course page)
2022/2023: Fundamentals of Machine Learning (6 ECTS), M.Sc. in Computer Science (LM-18) and M.Sc. in Physics (LM-17) (course page)
2021/2022: Machine Learning (3 ECTS), M.Sc. in Computer Science (LM-18) (course page)
2020/2021: Machine Learning (3 ECTS), M.Sc. in Computer Science (LM-18) (course page)
2019/2020: Machine Learning (3 ECTS), M.Sc. in Computer Science (LM-18) (course page)
2018/2019: Machine Learning, M.Sc. in Computer Science (LM-18) (course page)
2014/2015: Distributed Calculus and Coordination (3 ECTS), M.Sc. in Computer Science (LM-18)

Bachelor’s courses

2017/2018: Algoritmi e strutture dati, laboratorio (6 ECTS), B.Sc. in Computer Science (L-31), University of Camerino (course page)
2014/2015: academic tutor for Reti Logiche, B.Sc. in Computer Science (L-31), University of Camerino

Other teaching

2020/2021: Fondamenti di Data Analytics e Machine Learning, first-level specialisation Master Digital Solution Manager, University of Camerino (course page)
2019/2020 and 2020/2021: Cultore della materia for Metodi e tecnologie per l’insegnamento della Matematica (6 ECTS), M.Sc. in Science Education, University of Macerata

Lecture notes

Slides of the Machine Intelligence lecture series (PDF, protected):

Ph.D. supervision

2023–2027: Martina Zannotti (supervisor), graph neural networks for recommendation systems
2023–2027: Matteo Leonesi (co-supervisor), trustworthy AI and small language models
2021–2024: Vincenzo Nucci (co-supervisor), Unsupervised learning under the presence of data drift
2018–2021: Leonardo Vito (co-supervisor), Designing and implementing data science pipelines in healthcare and biological applications

Master’s theses supervised

2026: Viola Serafini, Vision Transformers for line segment and circular arc detection in industrial environments
2026: Noemi Miriello, Convolutional neural networks for line detection in industrial images
2023: Pietro Angelici, Enhancing documents review through knowledge graphs and large language models
2022: Tommaso Carletti, A cycle basis approach to persistent homology computation
2022: Riccardo Coltrinari, Neural machine translation: from natural language requirements to linear temporal logic formulas
2022: Marco Scarpetta, Online anomaly detection on streaming data
2022: Alessandro Antinori, RAINFALL 2.0: a rapid and interactive framework for ML dataflow generation applied to manufacturing scenarios
2022: Matteo Molteni, Case-based recommender system for university elective courses
2021: Andrea Falaschini and Manuel Cretone, CIAS: Clinical Intelligent & Adaptive System
2021: Giacomo Rocchetti, Towards a new framework for real-time self-adaptivity
2020: Simone Dominici, xML through formal verification: a comparison among different strategies
2020: Vincenzo Nucci, XTDA: a topological pipeline for understanding the behaviour of deep networks at global scale

I have also supervised eighteen Bachelor’s theses (2018–2023) on deep learning, motion detection, persistent homology tools, sequence analysis and AI ethics.