Machine learning (ML) is one of the most dynamic and exciting areas in modern data-driven research. Built upon inspiration from fields as different as statistics, computer science, neurosciences and physics, it allows for automatic learning from complex large-scale datasets. The lectures aim at introducing the core concepts and algorithms of ML in a way easily understood by physicists, both in the setting of supervised learning (linear and logistic regression, ensemble methods, deep neural networks…) and unsupervised learning (dimensional reduction, clustering, generative modelling…). The course is accompanied by Python Jupiter Notebooks, which allow for testing the main algorithms presented in the lectures, and introduces some highly used ML Python packages to the students.

READING MATERIALS: P Mehta, M Bukov, CH Wang, AGR Day, C Richardson, CK Fisher, D Schwab, A high-bias, low-variance introduction to Machine Learning for physicists, Physics Reports (2019) 1-124. STUDY MATERIALS: can be found at https://physics-complex-systems.fr/cursus.html

Modalità di esame: Prova scritta (in aula);

Exam: Written test;

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Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.