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Computational Fluid Dynamics

Introduction to Machine Learning

Objective: Introduce the students to the machine learning fundamentals, to the main techniques on supervised learning, and to the principal application domains. Present evolutionary calculation. The course explains how to design, develop and evaluate simple ML-based end-to-end systems and, at the same time, how to describe their operations.

Numerical Analysis

Objective: providing numerical analysis tools for scientific computing, with particular attention to linear algebra, polynomial approximation, numerical integration, numerical solution of ordinary differential equations and partial differential equations, approximation of eigenvalues and eigenvectors.

Optimisation Models

Objective: providing students with the methodological, theoretical and practical tools to formulate linear programming models and combinatorial optimization problems and to solve them, even for high dimensionality problems, using appropriate optimization software.

Advanced Mathematical Methods

Objectives: training students from different disciplines, such as applied mathematics, physics, engineering, to integrate theory and models in the study of some problems arising in applied sciences and which result in partial differential equation. Provide students with a mathematical background suitable to analyze them.