Advanced Deep Learning (ADL)

Course content

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, information retrieval along with web search, natural language processing tasks such as automatic translation, and bioinformatics. This course will give you detailed insight into deep learning, covering algorithms, theory and tools in this exciting field.


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

Learning outcome

Knowledge of

  • Convolutional neural networks

  • Recurrent neural networks

  • Generative neural networks, such as

    • Variational autoencoders

    • Generative adversarial networks (GANs)

  • Theory of deep learning

  • Topics in deep learning, exemplified by

    • Fully convolutional neural networks

    • Graph neural networks

    • Representation learning

    • Diffusion models

    • Self-supervised learning


Skills to

  • Select appropriate methodology to solve deep learning problems

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

  • 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 advanced deep models

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

  • Analyse deep learning algorithms

The course will mix lectures, exercise classes, and project work.

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 these courses include basic deep learning.
5. Programming experience in Python.

The course is similar to the discontinued courses NDAB20002U Advanced Deep Learning (ADL) and to NDAB21009U. Therefore you cannot register for this course , if you have already passed NDAB21009U - Advanced Deep Learning (ADL)or NDAB20002U Artificial Intelligence (AI).

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, the use of GitHub Copilot or similar AI-based programming tools is permitted.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

According to learning outcomes.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 32
  • Preparation
  • 68
  • Exercises
  • 46
  • Exam
  • 60
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 4
No limit.
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Stefan Sommer   (6-84807e7e768351757a3f7c863f757c)
Saved on the 14-09-2023

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students