NDAK15005U Information Retrieval (IR)
MSc Programme in Computer Science
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 combing evidence
- Domain-specific ranking
- Evaluation and optimisation
Knowledge
- Identify and explain the basic architecture of retrieval systems
- Identify and explain the basic models and techniques of collecting, storing and ranking information
- Identify and explain different criteria for information retrieval evaluation
Skills
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, and
- Design and calibrate appropriate solutions
Competences
- 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 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.
as show initiative in their assignments.
- Category
- Hours
- Lectures
- 28
- Practical exercises
- 57
- Preparation
- 14
- Project work
- 50
- Theory exercises
- 57
- Total
- 206
- Credit
- 7,5 ECTS
- Type of assessment
- PortfolioSeveral 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. - Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
Students will need to submit new projects/assignments; additionally an 20-minute oral examination without preparation will be administered covering the full course syllabus.
Criteria for exam assesment
Related to the learning outcomes
Course information
- Language
- English
- Course code
- NDAK15005U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- C
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Christina Lioma (7-75407e7b817f7352767b407d8740767d)