DM873: Deep Learning
The Study Board for Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340031102
Assessment: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master
STADS ID (UVA): N340031101
ECTS value: 10
Date of Approval: 07-04-2025
Duration: 1 semester
Version: Approved - active
Comment
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
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:
- Have competences in programming in Python
- Basic understanding of linear algebra
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.
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.
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
Content
The following main topics are contained in the course:
- feedforward neural networks
- recurrent neural networks
- convolutional neural networks
- backpropagation algorithm
- regularization
- factor analysis
- autoencoders
Literature
Examination regulations
Exam element a)
Timing
Autumn and January
Tests
Portfolio
EKA
N340031102
Assessment
Second examiner: External
Grading
7-point grading scale
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Duration
Oral exam - 15 minutes
Examination aids
1 page of notes + slides/presentation aids allowed
ECTS value
10
Additional information
Portfolio exam consisting of two parts:
- Two mandatory assignments handed in during the course
- Oral examination
The oral exam consists of a 5 min presentation of a randomly selected thematic topic (selected during the exam) from a preannounced list of thematic topics.The presentation is followed by questions/answers to potentially all topics of the course, including the assign-ments.
To achieve a passing grade overall, both elements 1 and 2 must individually meet the learning objectives.
The assessment of element 1 takes place in conjunction with the completion of element 2. The grade is primarily based on element 2, but element 1 can raise or lower the grade by one grade step.
Indicative number of lessons
Teaching Method
Planned lessons:
Total number of planned lessons: 48
Hereof:
Common lessons in classroom/auditorium: 48
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
Other planned teaching activities:
- Small take home exercises
- Study latest developments and approaches of deep learning by reading recent publications
Teacher responsible
Timetable
Administrative Unit
Team at Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.