MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
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.

Prerequisites

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

Learning Objectives

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

Contents of Module

 

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.

 

 

 

Teaching and Learning Methods

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.

Literature

Lecture slides, references to internet resources and books

Download full module description

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