Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
 1215 crediti ECTS in moduli tecnicoscientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati.  912 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione  69 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf)riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
 Insegnamento
 Documentazione
 Esame
Modules: 45
The goals of an organization can be efficiently pursued only through proper project management, as a mean able to consistently tackle their needs. Thus the role of the Project Manager become essential, as responsible to achieve the objectives, respecting the constraints determined by the project context. Modern Project Managers must have indepth technical and management knowledge.
The course provides the students…
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Scopri di piùIn an environment that is changing increasingly quickly, students will be taught the ability to assume societal responsibility either as engineers or in management functions in companies. They will develop a profound awareness of the moral and ethical aspects of their actions and also for the ecological and social impacts of companies. In their subsequent professional careers, they will be better able to judge the…
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Scopri di piùCompanies are increasingly interested in conducting their activities so that a longterm future is assured for its business, society and environment. The purpose of this class is to deal with the wellrecognized but sometimes vague concept of sustainability from an engineering perspective. The module is meant to introduce students to the implementation of sustainable management in industries and provide them with…
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Scopri di piùIn the Privacy and Law module, students gain an awareness of the threats to privacy in the fast changing digital society and are prompted to reflect on values in the historical and intercultural context.
Students acquire an overview (system and reference knowledge) of actual legal aspects that have not been specifically covered in either the vocational baccalaureate or in the Bachelor's degree course. In the…
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Scopri di piùThe CM_QRM addresses the most relevant basics in integrated quality and risk management. Theory is applied and specified by examples and case studies. The Module concentrates on current standards and best practices on quality and risk management and introduces the most established approaches.
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Scopri di più
Smart Service Design and Engineering  Value Creation:
 Basics of Smart Service Design (Customer insight, customer journey, value proposition design, use of data insights)
 Selected topics of Service Science and Service Dominant Logic
 Service blueprinting as a relevant step in the service engineering process
 Characteristics of Data Services and Data Products
 Use of data in the smart service design process and in…
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Scopri di piùAlgorithms are at the heart of every computer program. Informally, an algorithm is a procedure to solve a (computational) problem within a finite number of elementary steps. The same problem can be addressed with different algorithms, hence it is important to compare the different options in order to choose the best one. Experimental analysis is one way to perform such comparison, but it has several limits. The main…
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Scopri di piùStudents are introduced to statistical tools used in the industrial sector, and particularly in process and quality control. In this module, students learn to plan and conduct statistical evaluations independently.
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Scopri di piùAn algorithm is typically called efficient if its worstcase running time is polynomial in the size of the input. This course will focus on a huge and practically relevant family of problems, namely NPhard ones, for which (most likely) no efficient algorithm exists. This family includes fundamental problems in computational biology, network design, systems, computer vision, data mining, online markets, etc.
The…
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Scopri di piùMachine learning (ML) emerged out of artificial intelligence and computer science as the academic discipline concerned with “giving computers the ability to learn without being explicitly programmed” (A. Samuel, 1959). Today, it is the methodological driver behind the megatrend of digitalization. ML experts are highly sought after in industry and academia alike.
This course builds upon basic knowledge in math,…
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Scopri di piùModelling, simulation and optimization are fundamental to solving problems in a number of fields of science, technology and life. Students will learn to design, implement, simulate, and optimize a model of dynamic system. Simulation, the exploration of the dynamic behavior of the model in time and space, is discussed for both continuous and discreteevent systems. Simulating a model allows the evaluation of…
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Scopri di piùThis course offers an introduction to optimization, emphasizing basic methodologies and underlying mathematical structures. Optimization refers to the application of mathematical models and algorithms to decision making. A large number of quantitative realworld problems can be formulated and solved in this general framework. Applications of optimization comprise, for instance, decision problems in production…
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Scopri di piùThis course will provide an introductory review of the basic concepts of probability and statistics to understand probability distributions and to produce rigorous statistical analysis including estimation, hypothesis testing, and confidence intervals. Students will be introduced to the basic concepts of predictive modelling which by definition is the analysis of current and historical facts to make predictions about…
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Scopri di piùThe ubiquitous presence of uncertainty and noise in the engineering sciences makes it mandatory to understand and quantify random phenomena. To achieve this goal the course will provide a solid introduction to the theory of stochastic processes. Special attention is given to applications. The applications include examples from various fields such as communications and vision, signal processing and control, production…
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Scopri di piùOne of the most used (statistical) models for inferential data analysis is the linear regression model. But it is restricted to a Gaussian distributed response and a linear function for linking the linear combination of predictors with the expected response. Generalized Linear and Additive Models (GLM, GAM) allow us to relax some of these restrictions by specifying a more general set of response distributions and…
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Scopri di piùMany data sets are temporal by nature. The course shows how to analyze time series of different domains and how to develop statistical models based on the data, in order to forecast future values or classify the time series into predefined categories. A probabilistic approach is emphasized, i.e. it is also discussed how to compute the uncertainty of the forecast which has been made.
The course adopts a practical…
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Scopri di piùThis module introduces the main methods of text analysis using natural language processing (NLP) techniques, from a computer / data science perspective. The methods are introduced in relation to concrete applications, in order to extract meaningful, structured knowledge in several dimensions from large amounts of unstructured texts. The knowledge and applications are complementary to those of information retrieval,…
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Scopri di piùAnalyzing images is a very complex task that has many important realworld applications. This module presents powerful techniques to extract information from images and 3D data, based on machine learning and deep learning methods. These methods are mostly used as “black boxes” and their inner workings are not discussed in much detail. The module provides an overview of many image analysis applications such as…
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Scopri di piùThe module is organised around 4 core subject areas:

Data Preprocessing

Data Classification

Clustering
 Complex Networks
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Scopri di piùThis course is about Data Engineering and Information Retrieval. It covers methods and technologies for managing, processing and analyzing potentially large and distributed data collections, including multimodel databases and NoSQL stores. And it covers also mastering data in unstructured form (full text search). The course consists of four parts: 1. Database management; 2. Data warehousing and data analytics…
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Scopri di piùDeep Learning is one of the most active subareas of Machine Learning and Artificial Intelligence at the moment. Gartner has placed it at the peak in its 2017 Hype Cycle and the trend is going on. Deep Learning techniques are based on neural networks. They are at the core of a vast range of impressive applications, ranging from image classification, automated image captioning, language translation such as Google…
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Scopri di piùThe goal of this module is to help students to further develop their knowledge and skills in academic writing and presenting through the medium of English. Students will learn what it means to write advanced academic texts and to present them to an audience in an accurate, appropriate and convincing manner. The module is divided into a writing and a speaking part.
The writing part of the module focuses on key…
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Scopri di piùThe goals of an organization can be efficiently pursued only through proper project management, as a means able to consistently tackle their needs. Thus the role of the Project Manager becomes essential, as responsible to achieve the objectives, respecting the constraints determined by the project context. Modern Project Managers must have indepth technical and management knowledge.
The course provides the students…
Scarica il descrittivo completo del modulo
Scopri di piùIn an environment that is changing increasingly quickly, students will be taught the ability to assume societal responsibility either as engineers or in management functions in companies. They will develop a profound awareness of the moral and ethical aspects of their actions and also for the ecological and social impacts of companies. In their subsequent professional careers, they will be better able to judge the…
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Scopri di piùThe module aims to explain the operational planning and management of innovations to students on the basis of an integrated innovation management model, as well as introducing them to the relevant concepts. This will enable students to establish links to various companyinternal and companyexternal interfaces as part of innovation projects and to correctly interpret and influence these. In this module, students will…
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Scopri di piùCompanies are increasingly interested in conducting their activities so that a longterm future is assured for its business, society and environment. The purpose of this class is to deal with the wellrecognized but sometimes vague concept of sustainability from an engineering perspective. The module is meant to introduce students to the implementation of sustainable management in industries and provide them with…
Scarica il descrittivo completo del modulo
Scopri di piùIn the Privacy and Law module, students gain an awareness of the threats to privacy in the fast changing digital society and are prompted to reflect on values in the historical and intercultural context.
Students acquire an overview (system and reference knowledge) of actual legal aspects that have not been specifically covered in either the vocational baccalaureate or in the Bachelor's degree course. In the…
Scarica il descrittivo completo del modulo
Scopri di piùThe CM_QRM addresses the most relevant basics in integrated quality and risk management. Theory is applied and specified by examples and case studies. The Module concentrates on current standards and best practices on quality and risk management and introduces the most established approaches.
Scarica il descrittivo completo del modulo
Scopri di più
Smart Service Design and Engineering  Value Creation:
 Basics of Smart Service Design (Customer insight, customer journey, value proposition design, use of data insights)
 Selected topics of Service Science and Service Dominant Logic
 Service blueprinting as a relevant step in the service engineering process
 Characteristics of Data Services and Data Products
 Use of data in the smart service design process and in…
Scarica il descrittivo completo del modulo
Scopri di piùAlgorithms are at the heart of every computer program. Informally, an algorithm is a procedure to solve a (computational) problem within a finite number of elementary steps. The same problem can be addressed with different algorithms, hence it is important to compare the different options in order to choose the best one. Experimental analysis is one way to perform such comparison, but it has several limits. The main…
Scarica il descrittivo completo del modulo
Scopri di piùStudents are introduced to statistical tools used in the industrial sector, and particularly in process and quality control. In this module, students learn to plan and conduct statistical evaluations independently.
Please note: An MSE cursus may not contain both similar statistics modules FTP_AppStat and FTP_PredMod. Students can only choose one of these modules.
Scarica il descrittivo completo del modulo
Scopri di piùAn algorithm is typically called efficient if its worstcase running time is polynomial in the size of the input. This course will focus on a huge and practically relevant family of problems, namely NPhard ones, for which (most likely) no efficient algorithm exists. This family includes fundamental problems in computational biology, network design, systems, computer vision, data mining, online markets, etc.
The…
Scarica il descrittivo completo del modulo
Scopri di piùMachine learning (ML) emerged out of artificial intelligence and computer science as the academic discipline concerned with “giving computers the ability to learn without being explicitly programmed” (A. Samuel, 1959). Today, it is the methodological driver behind the megatrend of digitalization. ML experts are highly sought after in industry and academia alike.
This course builds upon basic knowledge in math,…
Scarica il descrittivo completo del modulo
Scopri di piùModelling, simulation and optimization are fundamental to solving problems in a number of fields of science, technology and life. Students will learn to design, implement, simulate, and optimize a model of dynamic system. Simulation, the exploration of the dynamic behavior of the model in time and space, is discussed for both continuous and discreteevent systems. Simulating a model allows the evaluation of…
Scarica il descrittivo completo del modulo
Scopri di piùThis course offers an introduction to optimization, emphasizing basic methodologies and underlying mathematical structures. Optimization refers to the application of mathematical models and algorithms to decision making. A large number of quantitative realworld problems can be formulated and solved in this general framework. Applications of optimization comprise, for instance, decision problems in production…
Scarica il descrittivo completo del modulo
Scopri di piùThis course will provide an introductory review of the basic concepts of probability and statistics to understand probability distributions and to produce rigorous statistical analysis including estimation, hypothesis testing, and confidence intervals. Students will be introduced to the basic concepts of predictive modelling which by definition is the analysis of current and historical facts to make predictions about…
Scarica il descrittivo completo del modulo
Scopri di piùStudents learn and experience an advanced approach to designing an autonomous realtime process monitoring system (cyberphysical system)
Scarica il descrittivo completo del modulo
Scopri di piùThe ubiquitous presence of uncertainty and noise in the engineering sciences makes it mandatory to understand and quantify random phenomena. To achieve this goal the course will provide a solid introduction to the theory of stochastic processes. Special attention is given to applications. The applications include examples from various fields such as communications and vision, signal processing and control, production…
Scarica il descrittivo completo del modulo
Scopri di piùOne of the most used (statistical) models for inferential data analysis is the linear regression model. But it is restricted to a Gaussian distributed response and a linear function for linking the linear combination of predictors with the expected response. Generalized Linear and Additive Models (GLM, GAM) allow us to relax some of these restrictions by specifying a more general set of response distributions and…
Scarica il descrittivo completo del modulo
Scopri di piùMany data sets are temporal by nature.
The first part of the course presents techniques for analysis of time series. It starts from visualization techniques; then it shows techniques for characterizing trend and seasonality; eventually it present structured statistical approaches based on exponential smoothing and arima techniques. Several examples referring to real data sets are shown.
In the second part of the…
Scarica il descrittivo completo del modulo
Scopri di piùThis module introduces the main methods of text analysis using natural language processing (NLP) techniques, from a computer / data science perspective. The methods are introduced in relation to concrete applications, in order to extract meaningful, structured knowledge in several dimensions from large amounts of unstructured texts. The knowledge and applications are complementary to those of information retrieval,…
Scarica il descrittivo completo del modulo
Scopri di piùAnalyzing images is a very complex task that has many important realworld applications. This module presents powerful techniques to extract information from images and 3D data, based on machine learning and deep learning methods. These methods are mostly used as “black boxes” and their inner workings are not discussed in much detail. The module provides an overview of many image analysis applications such as…
Scarica il descrittivo completo del modulo
Scopri di piùThe module is organised around 4 core subject areas:
 Data Preprocessing
 Data Classification
 Clustering
 Complex Networks
Scarica il descrittivo completo del modulo
Scopri di piùThis course is centered on the Data Engineering domain.
This course covers modern methods and technologies that are needed to manage and process potentially large, heterogeneous and distributed data collections. It includes diverse technologies frequently used in industrial contexts such as data warehouses, multimodel databases and NoSQL stores. A focus of the class is also given on Information Retrieval including…
Scarica il descrittivo completo del modulo
Scopri di piùDeep Learning is one of the most active subareas of Machine Learning and Artificial Intelligence at the moment. Gartner has placed it at the peak in its 2017 Hype Cycle and the trend is going on. Deep Learning techniques are based on neural networks. They are at the core of a vast range of impressive applications, ranging from image classification, automated image captioning, language translation such as Google…
Scarica il descrittivo completo del modulo
Scopri di più