MSE Master of Science in Engineering

The Swiss engineering master's degree


Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 

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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Ethics and Corporate Responsibility (CM_Ethics)

In 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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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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Privacy and Law (CM_PrivLaw)

In 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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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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Applied Statistics and Data Analysis (FTP_AppStat)

Students 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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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.

The…

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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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Data Management (TSM_DataMgmt)

This 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 multi-model 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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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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