Finite Difference Methods for Fluid Mechanics - Prof. Mario DI RENZO
This course will describe the finite difference numerical methods utilized in computational fluid dynamics. After a brief classification of the kind of partial differential equations that could be encountered while studying fluid mechanics, fundamental numerical schemes for the discretization of linear equations will be analyzed via theory and numerical experiments.
The course will also discuss the introduction of non-linearities and high-order methods.
The course will also discuss the introduction of non-linearities and high-order methods.
Docente: Mario DI RENZO
Introduction to Data Mining and Machine Learning - Prof. Marco PULIMENO
This course offers an introduction to the fundamental concepts of machine learning, together with an analysis of the main techniques and algorithms used in this discipline. The analysis of theoretical frameworks will be complemented by the development of concrete examples in Python and related machine learning libraries.
Topics covered include the theoretical foundations of data learning, the design of machine learning projects, the procedure for training, evaluating and validating machine learning models, as well as classical learning paradigms and related algorithms.
Topics covered include the theoretical foundations of data learning, the design of machine learning projects, the procedure for training, evaluating and validating machine learning models, as well as classical learning paradigms and related algorithms.
Docente: Marco PULIMENO