Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Course INF-24-52-K-6

Information Retrieval and Data Mining (2V+1U, 4.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 4.0 CP 78 h
2 V Lecture 28 h
1 U Exercise class (in small groups) 14 h
(2V+1U) 4.0 CP 42 h 78 h


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. irreg. SuSe
Level [6] Master (General)
Language [EN] English
Area of study [INF-INSY] Information Systems
Livecycle-State [NORM] Active

Possible Study achievement

  • Verification of study performance: proof of successful participation in the exercise classes (ungraded)


  • Boolean Information Retrieval (IR), TF-IDF)
  • Evaluation Models (Precision, Recall, MAP, NDCG)
  • Probabilistic IR, BM25
  • Hypothesis testing
  • Statistical language models
  • Latent topic models (LSI, pLSI, LDA)
  • Relevance feedback, novelty & diversity
  • PageRank, HITS
  • Spam detection, social networks
  • Inverted lists
  • Index compression, top-k query processing
  • Frequent itemsets & association rules
  • Hierarchical, density-based, and co-clustering
  • Decision trees and Naive Bayes
  • Support vector machines


  • Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze. Introduction to Information Retrieval, Cambridge University Press, 2008
  • Larry Wasserman. All of Statistics, Springer, 2004.
  • Stefan Büttcher, Charles L. A. Clarke, Gordon V. Cormack. Information Retrieval: Implementing and Evaluating Search Engines
  • Anand Rajaraman and Jeffrey D. Ullman. Mining of Massive Datasets, Cambridge University Press, 2011.
  • supplementary literature references will be given in the lecture

Requirements for attendance (informal)


Requirements for attendance (formal)


References to Course [INF-24-52-K-6]

Module Name Context
[INF-24-52-M-6] Information Retrieval and Data Mining P: Obligatory 2V+1U, 4.0 LP
Course-Pool Name
[INF-INSY_V-KPOOL-6] Lectures of the teaching area Information Systems