Deep Learning with Applications
A.Y. 2025/2026
Learning objectives
The course presents Deep Learning from a theoretical and practical point of view, introduces the basic elements of learning (non-linear models, minimization techniques, cross-validation and hyper-parameter tuning) and focuses on supervised, unsupervised and reinforcement learning models
Expected learning outcomes
At the end of the course the student will be able to:
- illustrate deep learning models in the learning context
supervised, unsupervised and for reinforcement.
- identify deep learning models suitable for the resolution of
problems in physics and beyond.
- use software and libraries for the development of deep learning models.
- illustrate deep learning models in the learning context
supervised, unsupervised and for reinforcement.
- identify deep learning models suitable for the resolution of
problems in physics and beyond.
- use software and libraries for the development of deep learning models.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
Second semester
Course syllabus
The syllabus is shared with the following courses:
- [FBP-102](https://www.unimi.it/en/ugov/of/af2026000fbp-102)
- [FBP-102](https://www.unimi.it/en/ugov/of/af2026000fbp-102)
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS - University credits: 6
Lessons: 42 hours
Professor:
Carrazza Stefano
Professor(s)