2021/2022

2023/2024

DM270

NN (INDEFINITO/INTERDISCIPLINARE)

DIPARTIMENTO DI MATEMATICA 'FELICE CASORATI'

ARTIFICIAL INTELLIGENCE

PERCORSO COMUNE

3°

Secondo Semestre (04/03/2024 - 18/06/2024)

3

36 ore di attività frontale

INGLESE

ORALE

GEROSA DAVIDE (titolare) - 3 CFU

Introduction to physics as provided in the relevant first- and second-year classes. Basic knowledge of the Python programming language.

- 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.

- 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.

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.

- 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.

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.

Lectures will take place at Milano-Bicocca.

Istruzione di qualità.
Uguaglianza di genere.
Industria, innovazione e infrastrutture.
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