Course syllabus spring 2024
Course syllabus spring 2024
Title
AI and Data Management for IoT
Swedish title
AI och datahantering för IoT
Course code
DA642E
Credits
15 credits
Grading scale
UA / Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U)
Language of instruction
English
Decision-making body
Faculty of Technology and Society
Establishment date
2022-03-08
Syllabus approval date
2022-02-14
Syllabus valid from
2023-01-16
Entry requirements
- 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)
Level
Advanced level
Main field
Computer Science
Progression level
A1F
Progression level in relation to degree requirements
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.
Course objectives
The students will develop their knowledge in the application of artificial intelligence, data processing methods and data storage systems related to IoT applications.
Course contents
- 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.
Learning outcomes
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.
Learning activities
Lectures, laboratory work, seminars, a project and independent studies.
Assessment
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.
Course literature
- 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.
Course evaluation
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).
Interim rules
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.
Additional information
The syllabus is a translation of a Swedish source text.