Deep Learning Systems


Content, progress and pedagogy of the module

Learning objectives


  • Have comprehension of the benefits of using deep learning models in its applications
  • Have comprehension of the supervised, semi-supervised, unsupervised and reinforcement deep learning-based models
  • Have fundamental knowledge on optimization techniques and the cost functions used in deep learning systems
  • Have fundamental knowledge on transformer and generative based deep learning models
  • Have fundamental knowledge on deep neural networks and computer vision


  • Be able to configure deep neural networks for training and inferencing
  • Be able to train a deep neural network on highly parallel architectures
  • Be able to select the appropriate hardware to perform efficient inferences
  • Be able to use deep learning models in simulation environments  
  • Be able to analyse the architecture of deep neural networks and their multiple layers
  • Be able to analyse typical case studies in the application of deep learning methods in domains such as computer vision and robotics
  • Be able to select the appropriate deep learning model for a given application
  • Be able to evaluate the performance of deep learning models


  • Be able to design and implement deep learning models in computer vision and robotics for industrial applications
  • Be able to use and apply deep learning frameworks to design deep learning models
  • Be able to evaluate different deep learning models within an application domain
  • Be able to adapt deep learning models to different domains

Type of instruction

The course will be taught by a mixture of lectures, workshops, exercises, mini-projects, self-study and e-learning.

Extent and expected workload

Since it is a 5 ECTS course module, the workload is expected to be 150 hours for the student.



Name of examDeep Learning Systems
Type of exam
Written or oral exam
Permitted aids
With certain aids:
For more information about permitted aids, please visit the course description in Moodle.
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 titleDeep Learning systemer
Module codeE-AIAS-K2-2
Module typeCourse
Duration1 semester
Language of instructionEnglish
Location of the lectureCampus Esbjerg
Responsible for the module


Education ownerMaster of Science (MSc) in Engineering (Advanced Power Electronics)
Study BoardStudy Board of Build, Energy, Electronics and Mechanics in Esbjerg
DepartmentDepartment of Energy
FacultyThe Faculty of Engineering and Science