DM873: Deep Learning

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340031112, N340031102
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340031101
ECTS value: 10

Date of Approval: 10-03-2020

Duration: 1 semester

Version: Approved - active


The course is an elective for students in the following programme: Computer Science, Mathematics, Applied Math, Computational BioMedicine.
The course is co-read with DS809: Deep Learning 

Entry requirements

The course cannot be chosen by students who have taken DS809: Deep Learning (5 ECTS), DM568: Deep Learning (summer school) (5 ECTS), AI506 or DS833.

Academic preconditions

Students taking the course are expected to:

  • To be profound programmers
  • Basic understanding of linear algebra

Course introduction

Machine learning has become a part in our everydays life, from simple product recommendations to personal electronic assistant to self-driving cars. More recently, through the advent of potent hardware and cheap computational power, “Deep Learning” has become a popular and powerful tool for learning from complex, large-scale data.
In this course, we will discuss the fundamentals of deep learning and its application to various different fields. We will learn about the power but also the limitations of these deep neural networks. At the end of the course, the students will have significant familiarity with the subject and will be able to apply the learned techniques to a broad range of different fields.

The course builds partly on the knowledge acquired in the course DM555 but can be taken by any Computer Science or Computational BioMedicine Master student.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • giving the competence to plan and execute a deep learning task by means of deep neural networks.
  • providing knowledge on the different types of deep learning approaches including their advantages and disadvantages.
  • transfer learned methods to new fields of applications.
  • challenges the student with real-life datasets and problem-solving skills

Expected learning outcome

The learning objectives of the course is that the student demonstrates the ability to:
  • Describe the principles of deep neural networks in a scientific and precise language and notation
  • Analyze the various types of neural networks, the different layers and their interplay 
  • Describe the feasibility of deep learning approaches to concrete problems
  • Understand the theoretical mathematical foundations of the field 
  • Apply deep learning frameworks for solving concrete problems


The following main topics are contained in the course:
  • feedforward neural networks
  • recurrent neural networks
  • convolutional neural networks
  • backpropagation algorithm
  • regularization
  • factor analysis
  • autoencoders


See itslearning for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)








Second examiner: None




Full name and SDU username


Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value


Additional information

The prerequisite examination is a prerequisite for participation in exam element a)

Exam element a)




Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N340031101, DM873: Deep Learning


Oral exam




Second examiner: External


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value


Indicative number of lessons

48 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
  • Intro phase (lectures, class lessons) - 32 hours
  • Training phase: 16 hours, including 16 hours tutorials
The course will consist of frontal lectures supported by discussion sessions. The students will get accompanying exercises demonstrating the collected knowledge on practical real-world problems. The student activation is completed by a mandatory project and discussions of current state-of-the-art research papers during the study phase.

Study activities:
  • Small take home exercises
  • Study latest developments and approaches of deep learning by reading recent publications

Teacher responsible

Name E-mail Department
Richard Röttger Data Science


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period