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Distributed information systems

CS-423

Lecturer(s) :

Aberer Karl

Language:

English

Summary

This course introduces the key concepts and algorithms from the areas of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.

Content

 

Information Retrieval

  1. Information Retrieval - Introduction 
  2. Text-Based Information Retrieval
  3. Vector Space Retrieval 
  4. Inverted Files
  5. Distributed Retrieval
  6. Probabilistic Information Retrieval
  7. Query Expansion
  8. Latent Semantic Indexing
  9. Word Embeddings
  10. Link-Based Ranking

Data Mining

  1. Data Mining ' Introduction
  2. Association Rule Mining
  3. Clustering
  4. Classification
  5. Classification Methodology
  6. Document Classification
  7. Recommender Systems
  8. Mining Social Graphs

Knowledge Bases

  1. Semi-structured data
  2. Semantic Web
  3. RDF Resource Description Framework
  4. Semantic Web Resources
  5. Keyphrase extraction
  6. Named entity recognition
  7. Information extraction
  8. Taxonomy Induction
  9. Entity Disambiguation
  10. Label Propagation
  11. Link Prediction
  12. Data Integration

 

Learning Prerequisites

Recommended courses

Introduction to Database Systems

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Ex cathedra + programming exercises (Python)

Assessment methods

25% Continuous evaluations with bonus system during the semester
75% Final written exam (180 min) during exam session

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14BCH2201    
14-15    
15-16BCH2201    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German