Deep Learning


Recommended prerequisite for participation in the module

The module builds on basic knowledge in probability and statistics theory, linear algebra and machine learning

Content, progress and pedagogy of the module

This course introduces deep learning both by presenting valuable methods and by addressing specific applications. This course covers both theory and practices for deep learning. The students will also have hands-on exercises experimenting with a variety of deep learning architectures for applications.

Learning objectives


Students must have knowledge about:

  • models and representation learning
  • Advanced topics including attention and transformer networks, autoencoders, generative adversarial networks, adversarial attacks, self-supervised learning, and deep reinforcement learning
  • Regularization, optimization, hyperparameter tuning, and data augmentation
  • Bias, fairness, and explainable AI
  • Design and implementation of deep learning for selected applications


  • Must be able to apply the taught methods to solve real-world problems.
  • Must be able to evaluate and compare the methods within a specific application problem.


  • Must have competences in analyzing a given problem and identifying appropriate deep learning methods to the problem.
  • Must have competences in understanding the strengths and weaknesses of the methods.

Type of instruction

As descript in §17


Prerequisite for enrollment for the exam

  • Submission of mini-project report


Name of examDeep Learning
Type of exam
Written or oral exam
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleDyb læring
Module codeESNNDSK2K3
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Study BoardStudy Board of Electronics and IT
DepartmentDepartment of Electronic Systems
FacultyThe Technical Faculty of IT and Design