• Bachelor of Science in Engineering (at least 180 credits) or a bachelor’s degree in computer science or related fields such as computer engineering, computer and information science, software engineering, informatics, telecommunications or electrical engineering.
• At least 15 credits in programming.
• Equivalent of English 6/English B in secondary school.
• Minimum of a passing grade from the course:Introduction to IoT (DA640E)

This course is part of the main field of computer science and may be included in the degree requirements for the master’s degree (120 credits) in computer science.

• Key concepts in AI
• Machine learning algorithms, including both supervised and unsupervised learning
• Techniques for data mining
• Autonomous agents and multiagent systems
• Distributed AI solutions for IoT systems
• Management of large amounts of data (big data) and data storage
• Data integration and data quality
• Personal privacy, ethical aspects of AI, such as algorithmic discrimination, impartial and unbiased processing of data, as well as legal aspects including GDPR, the right to be forgotten and data aggregation.

Knowledge and understanding
To pass the course, the student must be able to:

1. Describe AI concepts and methods for processing sensor data and making decisions in relation to IoT systems
2. Explain different choices of data storage technology for IoT applications

Skills and abilities
To pass the course, the student must be able to:

3. Apply AI methods to IoT systems, so that they become adaptive and learning
4. Analyse data from IoT devices and sensors with machine learning
5. Design and implement data collection, data storage and data retrieval solutions according to the requirements of an IoT application

Judgement and approach
To pass the course, the student must be able to:

6. Critically discuss the ethical and legal aspects related to the use of AI and data processing in IoT systems.

Lectures, laboratory work, seminars, a project and independent studies.

To achieve a passing grade for the course (A-E), all parts must have been completed with at least a grade E or G.

• Passed laboratory work and active participation in seminars (5 credits) – intended learning outcomes 3 & 4
• Passed written examination (5 credits) – intended learning outcomes 1, 2 & 6
• Passed project with reflection (5 credits) – intended learning outcomes 4, 5 & 6

The final grade is based on the use of criteria in an assessment matrix provided by the course coordinator. Laboratory work is assessed with UG while written examination and projects with reflection are assessed with A-U.

• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham: Morgan Kaufmann.
• Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. (3rd ed.) Boston: Pearson Education.

Reference literature:

• Harrison, G. (2016). Next Generation Databases: NoSQLand Big Data, Apress, 1st ed. Edition
• Relevant scientific articles on the topic of future ethics.

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format.

The syllabus is a translation of a Swedish source text.