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:

  • Insegnamento
  • Documentazione
  • Esame
Data Analysis and Classification (TSM_DataAnaCla)

The module is organised around 4 core subject areas:

  • Data Preprocessing
  • Data Classification
  • Clustering
  • Complex Networks

Requisiti

  • basic python scripting
  • basic mathematical analysis and linear algebra

Obiettivi di apprendimento

Students understand how to use database technologies and data analysis tools and languages to process large data collections.

  • They learn the basics of the analysis of large data sets
  • They know the main tools to address analysis of large data sets
  • They will learn and use the most common classification techniques
  • They will 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.

Categoria modulo

The content of the module is the following:

  • Introduction to data analysis
  • Data Preprocessing (noise and outliers, aggregation, PCA, features selection, etc. )
  • Linear Regression, Logistic Regression
  • Data Classification and classifier evaluation
  • Clustering and cluster validation
  • Recommendation Systems
  • Complex Networks

Metodologie di insegnamento e apprendimento

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, as required.

Bibliografia

Lecture slides, references to internet resources and books

Scarica il descrittivo completo del modulo

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