Course

Deep Neural Networks (ELE680)

In this course, you will be introduced to the foundations of deep learning, basic network structures and their applications and how to build, train and evaluate deep neural networks for different applications.


Dette er emnebeskrivelsen for studieåret 2025-2026

Fakta

Emnekode

ELE680

Vekting (stp)

5

Semester undervisningsstart

Autumn

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Autumn

Timeplan

Vis timeplan

Litteratur

Søk etter pensumlitteratur i Leganto

Content

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th for the autumn semester.

In this course, you will be introduced to the foundations of deep learning, basic network structures and their applications and how to build, train and evaluate deep neural networks for different applications. This includes:

  • Neurons, layers, back propagation, optimizers, loss functions, hyperparameters
  • Multilayer Perceptron Network (MPN)
  • Training a Neural Network
  • Unsupervised, supervised and semi-supervised learning approaches.
  • Transfer learning
  • Convolutional Neural Network (CNN)
  • Recurrent Neural Network (RNN) and Long Short-Term Memory networks (LSTMs)
  • Time Series analysis
  • Image classification and Object detection
  • Video Activity recognition
  • Autoencoders
  • Transformers
  • Text and Natural Language Processing

Learning outcome

At the end of this course the student should have a fundamental understanding of methods and deep neural network structures commonly used in deep learning. The student should also be able to build, train and evaluate models for one or more practical deep learning problems.

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

Machine Learning (ELE520)

Exam

Form of assessment Weight Duration Marks Aid
Project assessement 1/1 5 Weeks Letter grades All


The assigned project is carried out in groups of two students. Exceptionally, it can be one or three students per group. The report describes and documents work in the project. The report is made in collaboration with all the participants in the group and all participants will get the same grade.

There is no resit exam in this course. A new project report must be submitted the next time the course is taught.

Vilkår for å gå opp til eksamen/vurdering

2 assignments, Oral presentation of project

2 of 2 assignments need to be approved by course instructor within the specified deadlines.

Oral presentation of project is compulsory and is assessed as approved/ not approved.

Fagperson(er)

Course teacher:

Trygve Christian Eftestøl

Course teacher:

Kjersti Engan

Head of Department:

Tom Ryen

Course teacher:

Øyvind Meinich-Bache

Course coordinator:

Øyvind Meinich-Bache

Course teacher:

Vinay Jayarama Setty

Method of work

The course has a duration of approximately 12 weeks and will be completed early November. Lectures will be held the first 7 weeks. The students are expected to spend additional 5 hours a week on self-study and assignments.

The project will be carried out in the last five weeks of the course, and it is expected that each student spends about 10 hours per week.

Åpent for

Data Science - Master of Science Degree Programme Computational Engineering - Master of Science Degree Programme Computer Science - Master of Science Degree Programme Computer Science - Master of Science Degree Programme, Part-Time Cybernetics and Applied AI - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Emneevaluering

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Kontakt

Course teacher:

Trygve Christian Eftestøl

Course teacher:

Kjersti Engan

Head of Department:

Tom Ryen

Course teacher:

Øyvind Meinich-Bache

Course coordinator:

Øyvind Meinich-Bache

Course teacher:

Vinay Jayarama Setty

Tilbys av

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

The course description is retrieved from FS (Felles studentsystem). Version 1