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
Course code
ELE680
Version
1
Credits (ECTS)
5
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
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
Required prerequisite knowledge
Recommended prerequisites
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
Course teacher(s)
Course coordinator:
Øyvind Meinich-BacheCoordinator laboratory exercises:
Ketil OppedalCourse teacher:
Øyvind Meinich-BacheCourse teacher:
Vinay Jayarama SettyCourse teacher:
Mahdieh KhanmohammadiCourse teacher:
Krisztian BalogCourse teacher:
Trygve Christian EftestølCourse teacher:
Kjersti EnganHead of Department:
Tom RyenMethod 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.