Entry requirements

• CD120A Algorithms and Data Structures (7.5 credits)
• CM152A Mathematical Statistics (7.5 credits)
• 11 credits from the course CD640E Introduction to IoT (15 credits)

Progression level in relation to degree requirements

The course is part of the degree requirements for a Master of Science in Engineering in Computer Science and Engineering (specialisation IoT)

Course contents

The aim of the course is for the student to develop knowledge in the application of artificial intelligence (AI), data processing methods and data storage systems related to IoT applications.

The course contains the following components:

• Key concepts in AI
• Application of logic and logic programming for 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.

Learning outcomes

Knowledge and understanding

Upon completion of the course, the student shall be able to:

1. describe AI concepts and methods for processing sensor data and making decisions in relation to IoT systems, and

2. explain different choices of data storage technology for IoT applications.

Competence and skills

Upon completion of the course, the student shall 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, and

5. design and implement data collection, data storage and data retrieval solutions according to the requirements of an IoT application.

Judgement and approach

Upon completion of the course, the student shall be able to:

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

Learning activities

Lectures, seminars, lab sessions, project work and self-study.

Assessment

The following are required to pass the course

• passing grade in lab session work and active participation in seminars (5 credits, Pass/Fail) (Intended learning outcomes 3 and 4)
• passing grade on written examination (5 credits, UA) (Intended learning outcomes 2 and 6)
• passing grade on project with reflection (5 credits, UA) (Intended learning outcomes 4, 5 and 6)

For all assessments, the materials must be presented in a manner that makes it possible to discern individual performance.

The final grade is obtained by weighting the grades from a passed written exam and a passed project with reflection with the scope in credits.

Course literature

• Han, J., Kamber, M., & Pei, J. (2022). Data mining: Concepts and techniques (4th edition), Morgan Kaufmann.
• Russell, S. J. & Norvig, P. (2020). Artificial intelligence: A modern approach (4th edition), Pearson.

Reference literature

• Harrison, G. (2016). Next Generation Databases: NoSQLand Big Data. Apress.
• Relevant scientific articles

Course evaluation

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

Interim rules

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented. The syllabus is a translation of a Swedish source text.

Additional information

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.