Educational objectives ###################
General Objectives:
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Major advances in technology have resulted in the widespread implementation of information systems into businesses and organizations.
This course introduces languages, principles and methods of process modeling, analysis and innovation as critical factors to the
overall success of a business.
The course centers around the role of conceptual (sometimes referred as business) process modeling as a means to understand and capture the
workflows of interest in information systems of various kind. Students will learn the elements of process models and their precise meaning
using the Business Process Model and Notation (BPMN) international standard.
The course will cover processes within organizations (process orchestrations) and also interacting processes involving several organizations
(process choreographies), and will look at techniques to analyze and improve such processes from a formal perspective.
The course will also provide a basic knowledge and understanding of how to design, test and implement information systems for executable processes.
Finally, the course will present methods and tools to properly use process mining techniques, which enable to discover process models (whose structure
is unknown at the outset) starting from the logs recording the concrete events executed by the real workflows.
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Specific Objectives:
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Knowledge and understanding:
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At the end of the course, the students:
- learn the main methods to carry out a BPM (Business Process Management) project;
- are able to model a process with the BPMN standard;
- are able to implement and execute a process through a real information system;
- understand process mining algorithms and techniques.
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Applying knowledge and understanding:
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The students will be able to use suitable methodological and technological solutions for
(i) modeling a process in BPMN;
(ii) analysing it with quantitative techniques;
(iii) executing and monitoring it with an information system.
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Making judgements:
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The student acquires autonomy of judgment in proposing the most suitable approach to carry out a BPM project.
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Communication:
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The project activities and the lectures of the course allow the students to develop the proper abilities to communicate/share the design choices and
development methods for realizing any step of the business process life-cycle.
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Lifelong learning skills:
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In addition to the traditional learning skills provided by studying the teaching material, the project activities stimulate the student to
deepen her knowledge of the BPM topic, to improve the teamwork, and to the concrete application of the concepts and techniques investigated
during the course.
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Educational objectives KNOWLEDGE AND UNDERSTANDING.
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, based on Computational Intelligence techniques such as Bayesian learning, neural networks, fuzzy logic, evolutionary algorithms, etc., the student will reinforce the knowledge acquired in the first cycle of studies. The applications in Industrial Engineering for the solution of supervised and unsupervised problems, in particular regarding optimization, approximation, regression, interpolation, prediction, filtering, pattern recognition and classification, in order to elaborate and apply orginal ideas, will be further investigated also in a research context.
CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING.
Capability to analyze and solve problems related to the design, implementation and testing of machine learning algorithms, with particular reference to the development in Matlab/Python/TensorFlow environment, for developing machine learning solutions applied to problems of Industrial Engineering in the management, electrical, mechanical, logistics, biomedical fields and for the training of professional and business skills able to relate in the technical-scientific field of data analytics and business intelligence, in a context therefore broader than the field of Industrial Engineering.
MAKING AUTONOMOUS JUDGEMENTS.
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 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 applicatiion problems, starting from the limited information due to the organizational contingency of the course.
COMMUNICATE SKILLS.
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.
LEARNING SKILLS.
The didactic methodology implemented in the course requires an autonomous and self-managed activity of study during the development of vertical homeworks for the didactic and/or experimental investigation of some specific topics.
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