Information Retrieval (IR)

Course content

The course objective is to offer an advanced introduction into information retrieval. The goal is to understand and model how people search for, access and use information, in order to design and evaluate reliable retrieval algorithms. Through realistic and sound projects, the course aims to stimulate and prepare students for their MSc thesis work.


The course will focus on these main questions:

  • How can we design efficient retrieval systems?
  • How can we design effective retrieval systems?
  • How can we evaluate and improve system usability?


Content in detail:

Architecture of an IR system

  • Basic building blocks
  • Crawling, filtering and storing information
  • Ranking with indexes


Information ranking models

  • Probabilistic & machine learning models
  • Complex queries and combining evidence
  • Domain-specific ranking
  • Evaluation and optimisation



MSc Programme in Computer Science

Learning outcome

Knowledge of

  • The basic architecture of retrieval systems
  • The basic models and techniques for collecting, storing and ranking information
  • Different criteria for information retrieval evaluation


Skills in

Students should be able to transfer the above knowledge to real-world tasks by:

  • Designing appropriate strategies for crawling, storing and ranking information
  • Planning and carrying out appropriate evaluations


Given a working retrieval system, students should be able to:

  • Diagnose problems in its main information processing functions
  • Design and calibrate appropriate solutions


Competences to

  • Explain the basic information retrieval principles to both laymen and specialists
  • Use standard procedures and practices when designing or implementing information retrieval solutions
  • Present evaluation analyses and results in a proper format of a written report such that a technically qualified person can follow and obtain similar findings


The course will use a combination of lectures, lab sessions, class discussions and student presentations. Where possible, relevant guest lecturers will be involved.

The literature consists of seminal research and review articles from central journals and selected papers from peer-reviewed conferences, textbooks and research reports. This is supplemented with practical experience gained through lab sessions.

The literature will be listed in Absalon.

It is expected that students know how to program and have a working knowledge of Machine Learning.

The course is open only to students who have not previously passed "NDAK13001U Information Retrieval and Interaction"

Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Several elements will be included in the exam, the main ones being:
(i) submission of the student’s own project report, and
(ii) the student acting as opponent in respect of fellow students’ work.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 14
  • Practical exercises
  • 57
  • Theory exercises
  • 57
  • Project work
  • 50
  • English
  • 206