Objective: providing advanced knowledge of both theoretical and practical programming in C / C ++ and Python, with particular regard to the principles of object oriented programming and best practices of software development (advanced use of version control systems, continuous integration, unit testing), and introducing the modern technology of algorithms development.
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.
Objective: introducing to the main techniques for the design of algorithms and data structures to manipulate strings, trees and large graphs, in particular to compression techniques and randomization.
NOTE: The equivalent name of this course is "Algorithms for Massive Data".
This course will be available from the Academic Year 2022/23.
Objective: introducing students to the fundamentals and practice of stochastic modeling, simulation of stochastic models and inference of parameters starting from observations, with a focus on scalability for large models.
This course will be available from the Academic Year 2022/23.
Objective: introducing techniques of analysis and statistical Bayesian inference.
Objective: providing advanced notions of the theory of dynamical systems both in continuous and in discrete time. Introduce to modern techniques for the design of complex control systems with particular reference to application contexts of engineering interest in the industrial field.
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.
Objective: presenting the most important conceptual aspects of information retrieval systems and both principles and techniques for data visualization.
The course introduces the main concepts in information theory and algorithmic information theory.
The main ideas and methodologies of Reinforcement Learning will be introduced.
Objective: introduces the students to the design and analysis of Cyber-Physical Systems, we will see how to model such systems, how to specify and monitor their behaviors using formal languages as temporal logics, and how to use monitoring techniques for different applications as parameter synthesis and falsification test
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.
Objective: introducing students to computational management of data, in particular the characterization of an information system, data modeling, design and management of databases, including non-traditional ones (eg, unstructured documents, spatial data, biological data , multimedia data), to the fundamentals of distributed data and to methodologies and techniques for the management and analysis of big data.
Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.
Objective: introducing concepts and techniques for collaborative development of large and complex software systems for industrial applications, including Java, software development lifecycle, best practices in software development as code testing, versioning, and design patterns.
Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.