Main Goals. Through the introduction of the fundamentals on the theoretical, technical and practical aspects in the design and implementation of machine learning systems for the solution of problems regarding the analysis of signals, measurements and, more generally, of big data, namely through Computational Intelligence techniques based on neural networks, fuzzy logic, evolutionary algorithms, etc., the student will strengthen the knowledge acquired in the first cycle of studies. In this sense, applications in the field of Industrial and Information Engineering will be explored in depth for the solution of both supervised and unsupervised problems concerning optimization, approximation, regression, interpolation, prediction, filtering, recognition and classification, in order to process and apply original ideas even in a research context. The main goal of the course is to allow the student to develop machine learning systems through an appropriate formulation of the problem, a good choice of algorithms suitable for solving the problem and performing experiments in laboratory activities in order to evaluate the efficacy of the proposed solution. During the course, the main concepts and ideas that allow the effective use of machine learning algorithms in civil or industrial applications, rather than their purely mathematical formulation, will be mainly exposed. Therefore, the student will integrate the acquired knowledge to manage the complexity of an inductive learning mechanism where new knowledge is extracted and oriented to the solution of application problems, starting from the limited information due to the organizational contingency of the course.

Prerequisites. Fundamentals of Mathematics.

Expected Learning Outcomes. Capability to analyze and solve problems relating to the design, implementation and testing of Machine Learning algorithms, with particular reference to development in the Matlab/Python environment, for the creation of automatic learning systems applied to Industrial and Information Engineering problems in the management, electrical, mechanical, logistics, biomedical, aerospace fields, etc., as well as for the training of professional and business skills capable of relating in the technical-scientific context of data analytics and business intelligence in a context therefore broader than the specific sector of the student's engineering study. The topics covered in the course are of general interest in the scientific and industrial fields, in particular in the analysis of materials, in the design of devices and circuits, in automation and control systems, in the inversion of physical and abstract models for decision-making processes, in the management of complex networks (smart grids, energy and freight distribution, biological and social networks, etc.). Nonetheless, applications of new technologies will be introduced in the development of innovative computing systems, primarily quantum computers, in which the use of computational intelligence and machine learning algorithms for the effective and cutting-edge exploitation of the same ones is essential. Following this course, the student will be able to communicate the knowledge acquired to specialists and non-specialists in the world of work and research, where she/he will develop the subsequent scientific and/or professional activities.