Prerequisite/Recommended prerequisite for
participation in the module
The module is based on Linear algebra, calculus and probability
theory, and knowledge about programming in one or more of the
modern computer languages.
Content, progress and pedagogy of the
module
Learning objectives
Knowledge
- Have knowledge about the fundamental concepts and theories in
artificial intelligence (AI)
- Have knowledge about the algorithmic search and optimisation
techniques used in AI, such as depth-first, bread-first search and
gradient decent or particle swarm optimisation
- Have knowledge about how to model uncertainty in AI using
probabilistic methods and/or fuzzy logic
- Have knowledge about machine learning techniques, such as
artificial neural networks, Bayesian networks, clustering,
classification and its applications
Skills
- Be able to design AI based models and algorithms for specific
applications
- Be able to develop computer programs to implement one or more
of the techniques used in artificial intelligence
- Be able to design AI based solutions and implement them in an
embedded processor or computer
Competences
- Independently be able to apply modelling techniques in AI using
connectionist and/or probabilistic methods
- Independently develop artificial intelligence based system
solutions in specific problems
- Have a fundamental understanding of the modern techniques used
in AI, such as deep learning and its applications, for example in
big data problems
Type of instruction
Lectures with exercises supplemented with e-learning
activities.
Extent and expected workload
Since it is a 5 ECTS course module, the work load is expected to
be 150 hours for the student
Exam
Exams
Name of exam | Artificial Intelligence |
Type of exam | Written or oral exam |
ECTS | 5 |
Assessment | 7-point grading scale |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |