The Swiss engineering master's degree

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

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In an environment that is changing increasingly quickly, students will be taught the ability to assume social responsibility either as engineers or in management functions in companies. They will develop a profound awareness of the ethical aspects of their actions and for the ecological and social impacts of companies. In their subsequent professional careers, they will thus be better able to judge the consequences…

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The 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 company-internal and company-external interfaces as part of innovation projects and to correctly interpret and influence these. In this module, students will…

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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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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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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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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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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.

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.

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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 (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 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. First we will focus on the analysis of how different model structures can generate different behaviours, reproducing growth processes, but also goal seeking and oscillating behaviours. Then we focus…

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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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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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Students learn and experience an advanced approach to designing an autonomous real-time process monitoring system (cyber-physical system)

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The ubiquitous presence of uncertainty and noise in the engineering sciences and the importance of randomized algorithms in computer and data science make it mandatory to understand and quantify random phenomena. To achieve this goal the course will provide a solid review of probability theory and an introduction to the theory of stochastic processes. Special attention is given to applications, including examples…

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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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Many 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…

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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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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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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 for transactional or analytical use, 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…

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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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