This translation is for information purposes only. In the event of discrepancies, the Swedish-language version takes precedence.

IoT and Machine Learning Systems, 3.5 Credits

IoT och maskininlärningssystem, 3,5 Högskolepoäng

Established: 2025-09-11

Established by: Department of Engineering Science

Applies from: H26

Learning outcomes

After completing the course, the student should be able to:

Knowledge and understanding

• demonstrate knowledge and understanding of the Internet of Things (IoT) concept, including technologies such as IoT devices, IoT platforms, and wireless communication.
• explain the Industrial Internet of Things (IIoT) and how it can be utilized to transform existing systems.
• show understanding of how a machine learning system can be applied in conjunction with an IoT system.

Competence and skills

• design basic IoT systems for measurement and control.
• develop a machine learning system based on data collected from an IoT network.

Judgement and approach

• reflect on future ideas and visions within the field of Internet of Things.

Entry requirements

Degree of Bachelor of Science in computer engineering, electrical engineering, mechanical engineering, or industrial engineering and management. Additionally, the Bachelor of Science degree must be comprised of a minimum of 5 HE credits in programming and 15 HE credits in mathematics. Verified knowledge of English corresponding to the course English 6 or English level 2 in the Swedish Upper Secondary School (high school) or equivalent.

The forms of assessment of student performance

Group project with oral presentation. Individual written examination.

Course contents

The course provides a broad introduction to the concept of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), including the technologies that enable measurement, communication, and data storage. Furthermore, the course addresses the characteristics of IoT systems, their limitations, and the challenges associated with their implementation. It also includes an overview of how machine learning can be applied to analyze data generated by IoT systems.

Other regulations

Course grading: F/Fx/E/D/C/B/A - Insufficient, Insufficient- more work required before the credit can be awarded, Sufficient, Satisfactory, Good, Very Good, Excellent

Course language: The teaching is conducted in English.

General rules pertaining to examination at University West are available at www.hv.se.

If the student has a decision/recommendation on special support due to disability, the examiner has the right to examine the student in a customized examination form.

Cycle

Second cycle

Progressive specialization

A1N - Second cycle, has only first-cycle course/s as entry requirements

Main field of study

Automation, Production Technology