LABORATORY OF MACHINE LEARNING APPLIED TO PHYSICAL SYSTEMS
Stampa
Enrollment year
2021/2022
Academic year
2023/2024
Regulations
DM270
Academic discipline
NN (INDEFINITO/INTERDISCIPLINARE)
Department
DEPARTMENT OF MATHEMATICS "FELICE CASORATI"
Course
ARTIFICIAL INTELLIGENCE
Curriculum
PERCORSO COMUNE
Year of study
Period
2nd semester (04/03/2024 - 18/06/2024)
ECTS
3
Lesson hours
36 lesson hours
Language
English
Activity type
ORAL TEST
Teacher
GEROSA DAVIDE (titolare) - 3 ECTS
Prerequisites
Introduction to physics as provided in the relevant first- and second-year classes. Basic knowledge of the Python programming language.
Learning outcomes
- Describe physical systems using the appropriate mathematical formulation.
- Apply machine-learning algorithms to the resulting problem.
- Understand the advantages and limitations of machine learning algorithms given the specific problem at hand.
Course contents
- Intro: Computing and machine-learning in physics and astronomy.
- Intro: The typical tasks of a computational physicist.
- Example: A prototypical physical system.
- Task: The "classical" computational solution.
- Task: The machine-learning solution.
- Project development and reporting.
Teaching methods
Each class will pair traditional lectures (to introduce the relevant problems) with hands-on exercises and demonstrations (to tackle the relevant problem). These computational activities are the key content of the course.
Reccomended or required readings
- Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray. Princeton University Press
- Machine Learning for Physics and Astronomy. Viviana Acquaviva. Princeton University Press.
Assessment methods
Students will develop a computational project. This will be started during the lectures and completed asynchronously. The project report and associate code, likely in the form of a Jupyter notebook, will then be submitted for evaluation.
Further information
Lectures will take place at Milano-Bicocca.
Sustainable development goals - Agenda 2030
Istruzione di qualità.
Uguaglianza di genere.
Industria, innovazione e infrastrutture.
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