MSE Master of Science in Engineering

The Swiss engineering master's degree

Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:

  • ​​​​12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
    TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule.
  • 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
    FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können.
  • 6-9 ECTS in Kontextmodulen (CM)
    CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.

In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:

  • Unterricht
  • Dokumentation
  • Prüfung

Modules: 21

Advanced Project Management (CM_AdvProjMgmt)

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 in-depth technical and management knowledge.
The course provides the students…

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Ethik und Unternehmensverantwortung (CM_Ethics)

In einem sich immer schneller verändernden Umfeld werden die Studierenden befähigt, sowohl als Ingenieurinnen und Ingenieure als auch in Führungsfunktionen in Unternehmen gesellschaftliche Verantwortung zu übernehmen. Dazu werden sie vertieft für moralische und ethische Aspekte ihres Handelns sowie die ökologischen und sozialen Auswirkungen von Unternehmen sensibilisiert. So werden sie später im Berufsleben besser in…

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Integrated Sustainable Management of Production Systems (CM_IntSust)

Companies are increasingly interested in conducting their activities so that a long-term future is assured for its business, society and environment. The purpose of this class is to deal with the well-recognized 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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Privatsphäre und Recht (CM_PrivLaw)

Das Modul Privacy and Law sensibilisiert die Studierenden für die rechtlichen Fragen in der sich rasant digitalisierenden Gesellschaft und regt zu Reflexion über Werte im historischen und interkulturellen Kontext an.
Die Studierenden erhalten einen praxisnahen Überblick (System- und Orientierungswissen) betreffend aktuellen Rechtsaspekten, welche weder im Bereich der Berufsmatura noch der Bachelorausbildung speziell…

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Quality and Risk Management (CM_QRM)

The 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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Smart services (CM_SmartSer)


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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Advanced Algorithms and Data Structures (FTP_AdvAlgDS)

Algorithms 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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Angewandte Statistik und Datenanalyse (FTP_AppStat)

Den Studierenden werden statistische Werkzeuge vorgestellt, die im industriellen Sektor, insbesondere in der Prozess- und Qualitätskontrolle, benutzt werden. Das Modul befähigt die Studierenden, selbstständig statistische Auswertungen zu planen und durchzuführen.

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Approximation algorithms (FTP_ApprAlg)

An algorithm is typically called efficient if its worst-case running time is polynomial in the size of the input. This course will focus on a huge and practically relevant family of problems, namely NP-hard 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.


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Machine Learning (FTP_MachLe)

Machine 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 mega-trend 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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Modelling Simulation and Optimisation (FTP_ModSim)

Modelling, 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 discrete-event systems. Simulating a model allows the evaluation of…

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Optimization (FTP_Optimiz)

This 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 real-world problems can be formulated and solved in this general framework. Applications of optimization comprise, for instance, decision problems in production…

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Predictive Modelling (FTP_PredMod)

This 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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Stochastic Modelling (FTP_StochMod)

The 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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Advanced Statistical Data Analysis (TSM_AdvStDaAn)

One 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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Analysis of Sequential Data (TSM_AnSeqDa)

Many 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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Analysis of Text Data (TSM_AnTeDe)

This 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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Machine Learning in Computer Vision (TSM_CompVis)

Analyzing images is a very complex task that has many important real-world 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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Data Analysis and Classification (TSM_DataAnaCla)

The module is organised around 4 core subject areas:

  • Data Preprocessing
  • Data Classification
  • Clustering
  • Complex Networks

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Deep Learning (TSM_DeLearn)

Deep 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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