NDAK24002U Deep Learning (DL)

Volume 2024/2025

BSc Programme in Machine Learning and Data Science
MSc Programme in Computer Science
MSc Actuarial Mathematics
MSc Mathematics-Economics
Msc Statistics


Deep learning has pushed the boundaries in Artificial Intelligence (AI) and has been outperforming the state-of-the-art in numerous applications across a wide range of domains. These include object classification in images and natural language processing tasks such as automatic translation. This course will give you insight into the foundational methods in deep learning and techniques for effectively training deep networks.

Learning Outcome

Knowledge of

  • Convolutional neural networks

  • Transformers

  • Message passing and graph neural networks

  • Generative neural networks such as variational autoencoders

  • Basic strategies for interpretability of deep neural networks

  • Training methodology


Skills to

  • Select appropriate methodology to solve deep learning problems

  • Implement selected deep learning algorithms

  • Design and train deep learning algorithms


Competences to

  • Reflect upon the capabilities and limitations of deep learning algorithms

  • Recognise and describe possible applications of deep learning methodology

  • Design, optimise and use deep models

  • Apply the learned methodology to applications in analysis of real-world data such as images, sound and text

  • Analyse deep learning algorithms

See Absalon for course litterature.

Academic qualifications corresponding to:

1. Linear algebra corresponding to the course Lineær Algebra i datalogi (LinAlgDat).
2. Calculus corresponding to the courses Introduktion til matematik i naturvidenskab (MatIntroNat) and Matematisk Analyse (MatAn).
3. Basic statistics and probability theory corresponding to the course Sandsynlighedsregning og statistik (SS).
4. Machine learning corresponding to Machine Learning A (MLA). Please note that this course includes basic deep learning.
5. Programming experience in Python.
The course will mix lectures, exercise classes, and project work.
The course is similar to the discontinued courses Advanced Deep Learning (NDAK22002U and NDAB21009U) and Artifical Intelligence (NDAB20002U).
Therefore you cannot register for this course, if you have already passed Advanced Deep Learning (NDAK22002U or NDAB21009U) or Artifical Intelligence (NDAB20002U).
  • Category
  • Hours
  • Lectures
  • 32
  • Preparation
  • 68
  • Exercises
  • 46
  • Exam
  • 60
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Continuous assessment of 3-4 written assignments. All assignments must be passed. The final grade is based on an overall assessment.
All aids allowed

For programming tasks specifically, this includes AI-based programming tools such as github copilot or similar.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

The re-exam is 25 minutes oral examination, without preparation, in full course syllabus.

Criteria for exam assesment

See Learning outcome