Swedish name: Människocentrerad artificiell intelligens
This syllabus is valid: 2024-09-02
and until further notice
Course code: 5DV244
Credit points: 7.5
Education level: First cycle
Main Field of Study and progress level:
Computing Science: First cycle, has less than 60 credits in first-cycle course/s as entry requirements
Cognitive Science: First cycle, has less than 60 credits in first-cycle course/s as entry requirements
Established by: Faculty Board of Science and Technology, 2024-03-14
Contents
The course offers a broad introduction to Artificial Intelligence (AI) and its historical development from a Computing Science and technical perspective. The course focuses on the interaction between human and AI technology. It is aimed at students who want to understand how AI technologies actually work and what their fundamental limitations are.
Module 1, theory, 5.5 credits. The module provides a broad introduction to classical Artificial Intelligence (AI) and non-classical AI and their historical development. It addresses fundamental conditions, problems, and challenges for AI also from a philosophical and a human-centered perspective. Taking as a starting point human cognition, communication, and embodied action, the corresponding artificial intelligence methods, techniques, and their theoretical grounds are introduced. The purposes of AI techniques range from artificially perceiving the physical and social environment, classifying objects, learning from small and large sets of data, reasoning with contradictory information, making decisions, explaining the grounds for conclusions to a human or other AI systems and predicting the consequences of actions and decisions made by oneself as well as by others. The social and humanistic aspects of AI will be introduced. For instance, in artificial social intelligence manifested in interaction with humans, human values such as transparency, trust, fairness, and how biases are embedded and reinforced become important.
Module 2, pratice, 2 credits. In Module 2 some of the theories, methods and principles treated in Module 1 are illustrated and applied. This module consists of a set of mandatory assignments where the students will advance their understanding and skills in the practical application of AI methods while applying and advancing also their programming skills.
Expected learning outcomes
Knowledge and understanding After completing the course, the student should be able to:
(FSR 1) Give an overview of the field of artificial intelligence, its history, fundamental issues, challenges and main directions.
(FSR 2) Describe basic concepts, methods, and theories for rational agents.
(FSR 3) Explain why embodied cognition and situatedness are relevant for artificial intelligence.
(FSR 4) Explain basic concepts, methods, and theories for knowledge representation, automated reasoning, decision making, and planning.
(FSR 5) Explain basic concepts, methods, and theories of machine learning.
(FSR 6) Explain basic concepts, methods, and theories of large language models (LLMs) and generative artificial intelligence.
(FSR 7) Explain the main differences between artificial intelligence methods in terms of technology, societal applicability, limitations, and how they are complementary in hybrid artificial intelligence applications.
Competence and skills After completing the course, the student should be able to:
(FSR 8) Apply artificial intelligence concepts, methods, and theories to build basic intelligent software systems.
Judgement and approach After completing the course, the student should be able to:
(FSR 9) Assess the limitations of artificial intelligence methods.
(FSR 10) Discuss how human and societal values relate to artificial intelligence concepts, methods, and theories.
(FSR 11) Analyze and discuss the consequences of artificial intelligence technologies for the individual, the society, and the environment.
Required Knowledge
At least 7.5 ECTS programming and 7.5 ECTS data structures and algorithms.
Form of instruction
The instruction consists of lectures and computer-based assignments. In addition to scheduled activities, individual work with the material is required.
Examination modes
The assessment of Module 1 (FSR 1-7) is done by a written exam in halls. The grade scale is Fail (U), Pass (G), or Pass with Distinction (VG).
The assessment of Module 2 (FSR 8-11) is done by assignments presented both orally and in writing as well as seminars. The grade scale is Fail (U) or Pass (G).
On the course as a whole, the grade scale is Fail (U), Pass (G), or Pass with Distinction (VG). After both modules have been completed, the grade on the course as a whole will be the same as the grade on Module 1.
Adapted examination The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.
Other regulations
The course cannot be included in a degree together with the course 5DV124 or 5DV201 or 5DV243.
If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.
