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.


Course description for study year 2025-2026. Please note that changes may occur.

Facts

Course code

ELE680

Version

1

Credits (ECTS)

5

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Offered by
Time table

View course schedule

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.

Required prerequisite knowledge

None

Recommended prerequisites

ELE520 Machine Learning

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.

Coursework requirements

Oral presentation of project, 2 assignments

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.

Course teacher(s)

Course coordinator:

Øyvind Meinich-Bache

Course teacher:

Øyvind Meinich-Bache

Course teacher:

Vinay Jayarama Setty

Course teacher:

Trygve Christian Eftestøl

Course teacher:

Kjersti Engan

Head of Department:

Tom Ryen

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.

Course assessment

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.

Literature

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