Information Retrieval and Text Mining (DAT640)

The course offers an introduction to techniques and methods for processing, mining, and searching in massive text collections. The course considers a broad variety of applications and provides an opportunity for hands-on experimentation with state-of-the-art algorithms using existing software tools and data collections.

Course description for study year 2023-2024. Please note that changes may occur.


Course code




Credits (ECTS)


Semester tution start


Number of semesters


Exam semester


Language of instruction



  • Search engine architecture
  • Text preprocessing, indexing, representation learning
  • Retrieval models (vector-space model, probabilistic models, learning to rank, neural models)
  • Search engine evaluation
  • Query modeling, relevance feedback
  • Web search (crawling, indexing, link analysis)
  • Semantic search (knowledge bases, entity retrieval, entity linking)
  • Text categorization and clustering

Learning outcome


  • Theory and practice of concepts, methods, and techniques for managing and analyzing large amounts of text data.


  • Process and prepare large-scale textual data collections for retrieval and mining.
  • Apply retrieval, classification, and clustering methods to a range of information access problems.
  • Conduct performance evaluation and error analysis.

General competencies:

  • Understanding of the strengths and limitations of modern information retrieval and text mining techniques. Being able to identify promising business applications, participate in and lead such projects.

Required prerequisite knowledge



Project work and written exam

Form of assessment Weight Duration Marks Aid
Project work 2/5 Letter grades
Written exam 3/5 4 Hours Letter grades All aids are permitted - it is not permitted to collaborate / get help from other people in working with the exam task

The project is a combination of individual and group assignments. The project groups are set up by the course instructor. There is no re-sit option on the project. If a student fails the project, they have to take this part next time the subject is lectured.All assessment parts must be passed in order to achieve an overall grade in the course.

Course teacher(s)

Course coordinator:

Krisztian Balog

Course teacher:

Petra Galuscakova

Course teacher:

Krisztian Balog

Head of Department:

Tom Ryen

Method of work

6 hours of lectures/lab exercises each week.

Overlapping courses

Course Reduction (SP)
Web Search and Data Mining (DAT630_1) 5

Open for

Admission to Single Courses at the Faculty of Science and Technology Computer Science - Master of Science Degree Programme Industrial Automation and Signal Processing - Master's Degree Programme - 5 year Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


The syllabus can be found in Leganto