Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:

- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)

I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati. - 9-12 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 - 6-9 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: 27

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 academic texts and to present to an audience in an accurate, appropriate, and convincing manner. The module is divided into a writing and a presenting part.

The writing part of the module focuses on key document types…

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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 in-depth 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 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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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 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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Scopri di piùCompanies 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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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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**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 the…

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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 piùThis module introduces students with different categories of advanced algorithms and typical application areas.

In the first part of the module, the students will have a sound understanding of data structures and algorithms for efficiently handling either very large, complex or dynamic data sets or combinations thereof. They will be able to evaluate suitable algorithms and to apply them to typical tasks such as…

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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.

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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Scopri di piùAn 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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Scopri di piùMedical engineering is the intersection of many different disciplines. From engineering in its most varied forms, mechanics, electronics, computer science, management, to disciplines related to medicine: biology, anatomy, and physiology. In order to understand and put into practice the notions that the student will learn in this fascinating path, the same can not ignore the acquisition of basic knowledge about the…

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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 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 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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Scopri di piùNatural, social, and engineered complex systems can be modelled as being composed of agents interacting with one another and their environment. This course introduces students to the theory, tools and techniques for understanding and solving problems related to such systems.

The course is composed of two parts. In the first one, both cooperative and selfish agents and interactions between them will be discussed. The…

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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 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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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 purpose of this module is to enhance students' understanding of deep learning techniques.

We will explore significant and current developments in deep learning, including generative models, attention networks, transformers, graph neural networks and other related techniques.

Furthermore, we will examine case studies that pertain to language, speech, or visual processing domains.

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Scopri di piùThis module enables students to understand the main theoretical concepts that are relevant to text and speech processing, and to design applications which, one the one hand, find, classify or extract information from text or speech, and on the other hand, generate text or speech to summarize or translate language data, or in response to user instructions. The module briefly reviews fundamentals of natural language…

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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 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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Scopri di piùBayesian statistics provides an alternative viewpoint to the classical ‘frequentist’ statistics by using a different, more subjective interpretation of probability. This brings various advantages in solving typical industry problems, such as the inclusion of prior knowledge, more intuitive hypothesis tests or modeling uncertainty given small amounts of data. With increasing computational power, the popularity of…

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Automatising causal inference is one of the main challenges for making artificial intelligence (AI) reliable and thus really useful in the real world, as more and more emphasised by scientists and practitioners:

“Machines’ lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” (Judea Pearl, Turing Award winner and AI pioneer.)

“Causality is very…

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The module will address the theoretical aspects behind the realisation of Recommendation Systems and will allow students to practice over different use case scenarios. In particular it will address the following RecSys approaches:

- Traditional and Machine Learning based recommendation
- Deep Leanrning based recommendation
- Complex networks based recommendation.

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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 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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This module presents powerful techniques to manage the lifecycle of machine learning models, covering in particular baseline models, infrastructure (clusters, cloud, edge AI and resource management) and tooling (frameworks), model training and debugging, model evaluation and tuning, data management (sources, storage, versioning, privacy), systems testing (CI/CD) and explainability, deployment (batch, service, edge),…

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