MSE Master of Science in Engineering

The Swiss engineering master's degree

Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:

  • ​​​​12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
    TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule.
  • 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
    FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können.
  • 6-9 ECTS in Kontextmodulen (CM)
    CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.

In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:

  • Unterricht
  • Dokumentation
  • Prüfung
Multimodal Recommendation Systems and Complex Networks (TSM_DataAnaCla)

 

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.


 

 

 

 

Eintrittskompetenzen

 

  • Machine Learning and Data Mining, 
  • Python programming
  • basic calculus, linear algebra and statistics concepts

 

Lernziele

Students understand the theoretical aspects behind the realization of Recommendation Systems and they will learn how to build them over different use case scenarios.

They will learn how recommendation systems work, focusing on three different approaches:

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

They will learn how to deal with classical recommendation challenges like imbalanced data set problems, cold-start problems, and long tail problems. Additionally they will learn how to evaluate recommendation systems.

They will learn how to deal with complex networks and how to exploit network extracted information to enhance recommendation solutions.

Finally they will learn how to build multimodal recommendation systems exploiting social networks metrics and dynamics in order to deal with content spread and users engagement.

They know the current research directions within these domains.

They can reuse the material acquired in this course in their own working environment and apply them to solve their specific problems

Modulkategorie

 

The content of the module includes 3 main topics:

Complex Networks:

  • Network Elements (Handling Networks in Code,  Density and Sparsity, Subnetworks, Degree, Multilayer and Temporal Networks,  Network Representations)
  • Network measures (Hubs, Centrality Measures, Centrality Distributions, The Friendship Paradox, Ultra-Small Worlds, Robustness, Core Decomposition, Transitivity, Similarity)
  • Network models (Lattice, Random Networks, Small Worlds, Configuration Model, Preferential Attachment, Other Preferential Models)
  • Community Detection
  • Dynamic models (Ideas, Information, Influence, Epidemic Spreading, Opinion Dynamics,  Search)
  • Social Media as Networks (es. Twitter, Facebook and Reddit)

 

Recommendation Systems:

  • Traditional and Machine Learning based Recommendation Systems (Collaborative Filtering, Content Based, Knowledge Based, Hybrid)
  • Deep Learning based Recommendation Systems
  • Reinforcement Learning based Recommendation Systems
  • Evaluation of Recommendation Systems
  • Handling challenges in Recommendation Systems (imbalanced data set problems, cold-start problems, long tail problems)

Multimodal Systems for Recommendation:

  • Complex Networks/Social Networks integration
  • Use case: Recommendation Systems for Social Networks content spread and users engagement.

 

 

 

Lehr- und Lernmethoden

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.

Bibliografie

Lecture slides, references to internet resources and books

Vollständige Modulbeschreibung herunterladen

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