MSE Master of Science in Engineering

The Swiss engineering master's degree

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:

Data Analysis and Classification (TSM_DataAnaCla)

The module is organised around 4 core subject areas:

Students understand how to use data analysis tools to process large, structured and heterogeneous data collections.

They learn the basics of the data analysis
They know the main tools and techniques to address the analysis of large data sets
They learn and use the most common classification techniques
They learn how to exploit the networking structure of the data to handle the complexity and dynamicity of large set of data
They learn the main tools for data and results visualization
They learn methods for processing and clustering with the purpose of effective analysis
They can reuse the material acquired in this course in their own working environment and apply them to solve their specific problems
They know the current research directions within these domains.

The content of the module is the following:

Introduction to data analysis
Data Preprocessing (univariate and bivariate analysis, features selection, dimensionality reduction)
Linear Regression, Logistic Regression
Data Classification, Bagging and Boosting, classifiers evaluation
Clustering and clustering validation
Recommendation Systems
Complex Networks Theory
Network measures and Models

Problem based learning. During the lesson the lecturer will introduce real world problems and the class will try to solve them together.

The lecturer will support the problem solving process, introducing new concepts and tools, as required.

Practical work will complement the theory, so that students can put in practice the studied arguments.

Lecture slides, references to internet resources and books