IKT450 Deep Neural Networks (Autumn 2020)
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Morten Goodwin
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- ½ year
Table of contents
The course is connected to the following study programs
- Artificial Intelligence and The Internet of Things, Master's Programme
- Artificial Intelligence, 5-year master programme
- Master's Programme in Industrial Economics and Technology Management
Teaching language
English
Learning outcomes
In this course, the students will learn the foundations of neural networks and deep learning. They will, at course completion, be able to build and train feed-forward and recurrent neural models, understand the critical parameters in neural architectures, and understand when to apply different models. The students will also learn the theoretical foundation of the models, types of layers, and optimizers, and learn how to design, implement, and analyze advanced shallow and deep neural networks. They will gain practical programming experience with deep neural networks highly relevant to the industry.
Contents
The course covers theoretical and practical aspects of neural networks:
- fundamentals of neural networks including feed-forward models, optimizers, and training algorithms (e.g., backpropagation),
- neural network architectures including deep learning models, convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks, and
- working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects.
Teaching and learning methods
Combination of lectures, assignments, paper studies, lab, and report writing. The tasks are done individually or in small groups with group supervision.
The work load for the average student is approximately 200 hours.
Examinations
Graded portfolio assessment. Information about the content of the portfolio will be given in LMS by the start of the semester.
Student evaluation
The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.
Offered as a free-standing course
Yes, subject to availability.
Reduction of Credits
This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:
| Course | Reduction of Credits |
|---|---|
| IKT466 – Introduction to Machine Learning. | 4 |
Last updated from FS (Felles studentsystem)
Oct. 15, 2026 6:06:00 AM