DM863: Deep Learning

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340039112, N340039102
Censorship: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340039101
ECTS value: 5

Date of Approval: 26-10-2018


Duration: 1 semester

Version: Archive

Comment

15020001 (former UVA) is identical with this course description. 

Entry requirements

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


 

Content

The following main topics are contained in the course:


  • feedforward neural networks

  • recurrent neural networks

  • convolutional neural networks

  • backpropagation algorithm

  • regularization

  • factor analysis 


 

Literature

See Blackboard for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)

Timing

Spring

Tests

Compulsory assignments

EKA

N340039112

Censorship

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

June

Prerequisites

Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N340039101, DM863: Deep Learning

Tests

Oral exam

EKA

N340039102

Censorship

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

5

Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

36 hours per semester

Teaching Method

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.

Educational 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 roettger@imada.sdu.dk

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi, fiktiv)

Team at Registration & Legality

NAT

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