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: 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
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:
- 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
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
Prerequisites for participating in the exam a)
Timing
Autumn
Tests
Project
EKA
N340031112
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) | N340031101, DM873: Deep Learning |
Tests
Oral exam
EKA
N340031102
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
10
Indicative number of lessons
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