Course

Deep Neural Networks (ELE680)

Facts

Course code ELE680

Credits (ECTS) 5

Semester tuition start Autumn

Language of instruction English

Number of semesters 1

Exam semester Autumn

Time table View course schedule

Literature The syllabus can be found in Leganto

Introduction

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.

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

Machine Learning (ELE520)

Exam

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.

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.

Open for

Data Science - Master Computational Engineering - Master Computer Science - Master Computer Science - Master (Part-Time) Cybernetics and Applied AI - Master

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.

Contact

Head of Department:
Tom Ryen
Course coordinator:
Øyvind Meinich-Bache
Coordinator laboratory exercises:
Enrico Guerriero

Offered by

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

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