Deep Learning with Applications
A.Y. 2021/2022
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 cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Professor(s)