English

DM863: Deep Learning (5 ECTS)

STADS: 15020001

Level
Master's level course

Teaching period
The course is offered in the spring semester.

Teacher responsible

Email: roettger@imada.sdu.dk

Timetable

Group	Type	Day	Time	Classroom	Weeks	Comment
Common	I	Tuesday	16-18	IMADA semi	6
Common	I	Tuesday	14-16	U11	7	Moved class due to course clash.
Common	I	Tuesday	12-14	U145	8	Moved class due to course clash.
Common	I	Tuesday	12-14	IMADA semi	9-12
Common	I	Thursday	14-16	U145	6-12
Common	I	Thursday	10-15	T8	23	Recap session for DM863
H1	TE	Tuesday	12-14	IMADA semi	14
H1	TE	Wednesday	14-16	IMADA semi	7-12

Show entire timetable
Show personal time table for this course.

Comment:
Ubegrænset deltagerantal

Prerequisites:
None

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

Subject overview

The following main topics are contained in the course:

feedforward neural networks
recurrent neural networks
convolutional neural networks
backpropagation algorithm
regularization
factor analysis

Literature

Meddeles ved kursets start

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam

Compulsory assignments. Bestået/ikke-bestået, intern censur ved underviser. Forudsætningsprøven er en forudsætning for deltagelse i eksamenselement a).

Assessment and marking:

Oral exam. External marking, 7-mark scale. A closer description of the exam rules will be posted under 'Course Information' on Blackboard. (5 ECTS). (15020002).

Expected working hours
The teaching method is based on three phase model.
Intro phase: 24 hours
Skills training phase: 12 hours, hereof:
- Tutorials: 12 hours

Educational activities

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

Educational form
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.

Language
This course is taught in Danish or English, depending on the lecturer. However, if international students participate, the teaching language will always be English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.