DSK809: Deep Learning
The Study Board for Science
Teaching language: English
EKA: N340152212, N340152202
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Kolding
Offered in: Autumn
Level: Master
STADS ID (UVA): N340152201
ECTS value: 5
Date of Approval: 12-03-2025
Duration: 1 semester
Version: Archive
Entry requirements
The course can only be followed by Data Science students.
The course cannot be followed by students who have passed either DM863: Deep Learning (5 ECTS), DM873: Deep Learning (10 ECTS), DM568: Deep Learning (summer school) (5 ECTS), DS809 or DS833.
The course cannot be followed by students who have passed either DM863: Deep Learning (5 ECTS), DM873: Deep Learning (10 ECTS), DM568: Deep Learning (summer school) (5 ECTS), DS809 or DS833.
Academic preconditions
Academic preconditions. Students taking the course are expected to:
- Have basic understanding of linear algebra
- Have basic programming skills
The course builds partly on the knowledge acquired in the course DM566 or DSK804.
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
- 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
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
Autumn
Tests
Project
EKA
N340152212
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course.
ECTS value
0
Additional information
The prerequisite examination is a prerequisite for participation in exam element a)
Exam element a)
Timing
January
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Examination part | Prerequisites for participating in the exam a) | N340152201, DSK809: Deep Learning |
Tests
Oral examination
EKA
N340152202
Assessment
Second examiner: External
Grading
7-point grading scale
Identification
Student Identification Card - Name
Language
Normally, the same as teaching language
Examination aids
To be announced during the course.
ECTS value
5
Indicative number of lessons
Teaching Method
Planned lessons:
Total number of planned lessons: 36
Hereof:
Common lessons in classroom/auditorium: 36
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.