NDAK24003U Advanced Topics in Deep Learning (ATDL)

Volume 2026/2027
Education

MSc Programme in Computer Science

Content

This course provides a detailed insight into advanced deep learning methods, covering algorithms, theory, and tools in this rapidly evolving field.

As an advanced-level course, it builds on deep learning basics and therefore has machine learning and deep learning courses as prerequisites.

The focus will be on advanced deep generative models, graph neural networks, foundation models, and methods for efficient training and transfer learning.

The topics may vary from year to year, reflecting current trends in literature.
 

Learning Outcome

Knowledge of

Selected advanced topics in deep learning, including:

  • State-of-the-art deep models in specific domains

  • Design and analysis of deep learning algorithms

  • Theoretical foundations of deep learning

The exact list of topics will depend on the teachers and current research trends in deep learning. Topics will be announced on the course's Absalon page.

Skills to

  • Implement advanced deep learning algorithms using state-of-the-art tools

  • Read, understand, and assess recent scientific literature in deep learning

  • Apply knowledge obtained through research papers to practical and theoretical problems

  • Compare deep learning methods and evaluate their strengths and limitations

Competences to

  • Understand advanced deep learning methods and techniques

  • Design, optimize, and apply advanced deep learning models

  • Plan, structure, and carry out self-learning within the field
     

See Absalon.

Applicants are expected to have academic qualifications equivalent to a BSc degree.

The following courses are highly recommended:

- Machine learning: corresponding to Machine Learning A (MLA) and Deep Learning (DL)
- Linear algebra: corresponding to Linear Algebra in Computer Science (LinAlgDat)
- Calculus: corresponding to Mathematical Analysis and Probability Theory for Computer Scientists (MASD) and Modelling and Analysis of Data (MAD), or Introduction to Mathematics for Science (MatIntroNat) and Mathematical Analysis (MatAn)
- Statistics and probability theory: corresponding to Probability Theory and Statistics (SS)
- Programming: solid experience in Python
The course will mix lectures, exercise classes, and project work.
  • Category
  • Hours
  • Lectures
  • 28
  • Class Instruction
  • 14
  • Preparation
  • 70
  • Exercises
  • 94
  • Total
  • 206
Oral
Individual
Collective
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Continuous assessment of three written assignments and group presentations. All assignments must be passed. The final grade is based on an overall assessment of the assignments and presentations.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Internal examiner.
Re-exam

The re-exam is a 25-minute oral examination, without preparation, in the course syllabus.

Criteria for exam assesment

See Learning outcome