Table of contents

The course is connected to the following study programs

Teaching language

English

Learning outcomes

On successful completion of the course, the student should:

  • Have knowledge of the foundations of neural networks and deep learning.
  • 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.
  • Have knowledge of the theoretical foundation of the models, types of layers, and optimizers.
  • Be able to design, implement, and analyze advanced shallow and deep neural networks.
  • Have 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 Canvas by the start of the semester. Graded assessment. There will be a postponed exam in portfolio assessment.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes, subject to availability.

Admission Requirement if given as a free-standing course

Admission requirements for the course are the same as for the master’s programme in ICT.

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 10:14:14 AM