Deep Neural Networks (ELE680)

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


Course description for study year 2024-2025

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, the most effective and common 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
  • Unsupervised, supervised and semi-supervised learning approaches.
  • Transfer learning
  • Multilayer Perceptron Network (MPN)
  • Convolutional Neural Network (CNN)
  • Time Series analysis
  • Reccurent Neural Network (RNN) and Long Short-Term Memory networks (LSTMs)
  • Autoencoders

  • Natural language processing (NLP)

    • Natural Language Understanding (NLU) and Embeddings.
  • Image classification and Object detection
  • Video Activity recognition
  • Deep Reinforcement Learning
  • Deep learning in image reconstruction and medical imaging.
  • Transformers

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 Letter grades

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. An oral presentation of the project is a mandatory part of the project.There is no resit exam in this course. A new project report must be submitted the next time the course is taught.

Coursework requirements

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

Course teacher(s)

Course coordinator:

Øyvind Meinich-Bache

Coordinator laboratory exercises:

Ketil Oppedal

Course teacher:

Øyvind Meinich-Bache

Course teacher:

Vinay Jayarama Setty

Course teacher:

Mahdieh Khanmohammadi

Course teacher:

Krisztian Balog

Course teacher:

Trygve Christian Eftestøl

Course teacher:

Kjersti Engan

Head of Department:

Tom Ryen

Method of work

The course has a duration of approximately 8 weeks and will be completed mid October. Lectures will be held the first 5 weeks. The students are expected to spend additional 6-8 hours a week on self-study and assignments.

The project will be carried out in the last three weeks of the course and it is expected that each student spend about 15 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 subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

The syllabus can be found in Leganto