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
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: 

  • Filtering algorithms based 
  • Machine and Deep learning algorithms based 
  • Community detection algorithms based 


Requisiti

 

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

 

Obiettivi di apprendimento

Students

understand the theoretical aspects behind the realisation of Recommendation

Systems and they will learn how to build

them over different use case scenarios. They will learn three main recommendation systems approaches:

  • Filtering algorithms based 
  • Machine and Deep learning algorithms based 
  • Community detection algorithms based 

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.

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 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.


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 and tools, as required. 

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

Bibliografia

Lecture slides, references to internet resources and books

Scarica il descrittivo completo del modulo

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