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