Course - Digital Information Processing - IDIG4120
Digital Information Processing
New from the academic year 2026/2027
About
About the course
Course content
This course introduces the mathematical and computational foundations of digital information processing. Topics include:
- Fundamentals of digital signals and systems
- Data types and digital representation
- Information theory
- Data collection, quality assurance, and security
- Efficient data pipelines
Learning outcome
Knowledge
Students will be able to:
- Explain the theoretical foundations of digital signals and systems, including sampling, quantisation, and frequency analysis.
- Explain the role of linear algebra and data representation in digital information processing.
- Describe methods for data collection and quality assessment in large-scale visual datasets.
- Explain the principles of information theory and data compression relevant to digital information processing.
Skills
Students will be able to:
- Apply Fourier and convolutional techniques to process and analyse digital signals and images.
- Design efficient data processing pipelines for large-scale visual information.
- Implement algorithms for data transformation and compression using appropriate software tools.
- Evaluate trade-offs between compression and data quality for subsequent analysis.
Learning methods and activities
Learning will take place through lectures, seminars, and practical assignments, including exercises and small projects. Some sessions may include group discussions or guest lectures on related research topics.
Further on evaluation
(the information may be changed until June 15th)
- Group project presentation (40%)
- Oral exam (60%)
Grades are given on a scale from A to F. A re-sit examination will be offered, also oral.
Re-sit examination is offered for the oral exam. In the case the student fails this course, a re-sit exam will be conducted in March of the Spring following the course.
This course acknowledges the use of AI as part of assignments and deliverables. However, it requires an explicit declaration of how and where it is used. Details will be provided at the beginning of the course.
Specific conditions
Admission to a programme of study is required:
Informatics (MSIT)
Recommended previous knowledge
Basic programming, linear algebra and calculus.
Subject areas
- Computer Science