Students

Artificial Intelligence

2019-2020
Deze opleiding wordt aangeboden in het Engels. Sommige van de omschrijvingen zijn daarom mogelijk alleen in het Engels beschikbaar.

Our Artificial Intelligence (AI) Master programme is oriented towards Hybrid intelligence, where AI systems and humans collaborate. The AI master programme has a human-centric approach to AI. It is aimed at providing students with solid underpinnings in the technical and algorithmic aspects of AI, combined with a thorough understanding of human functioning from human-oriented sciences. 

Students approach the study of AI both from a technical perspective, focusing on the understanding, analysis and development of novel AI algorithms, and from a societal and human perspective, looking into questions such as “how can we develop and evaluate computer-based technology that exploits knowledge about human functioning?” and “how can human and AI-technology complement each other and collaborate with each other?” 

The first year includes broad courses that focus on core AI topics, while the second year is devoted to the specialisation phase.

During the specialisation phase, students deepen and broaden the knowledge of AI techniques by building up the knowledge obtained from the compulsory courses on the core AI topics. They can choose elective advanced courses in a more specialised AI topic (e.g. Planning and Reinforcement learning),  specific AI application area (e.g. support of people to have a healthy lifestyle and elderly care), or relevant scientific discipline (e.g. psychology, sociology, movement sciences, biomedical sciences, criminology, etc.). Another option is to continue in the specialised track “Cognitive Science”. This track provides students with a further in-depth understanding of the cognitive aspects of AI.

Graduates from the Master in Artificial Intelligence typically move on to pursue a career as a.o. Software Engineers, Data Scientists or Machine Learning Engineers at leading ICT companies such as IBM, Microsoft, Google, Facebook and Apple.

Info

Niveau Master
Taal Engels
Duur 2 years
Vorm Voltijd
Studiepunten 120 EC
Faculteit Faculteit der Bètawetenschappen
Deze opleiding wordt aangeboden in het Engels. Sommige van de omschrijvingen zijn daarom mogelijk alleen in het Engels beschikbaar.
Artificial Intelligence track Cognitive Science
Omschrijving
The Cognitive Science track focuses on the study of human cognition
through computational methods. It is jointly organised by the Department
of Cognitive Psychology (Faculty of Behavioural and Movement Sciences),
and the Department of Computer Science (Faculty of Sciences), and
includes courses from both departments.
Students in Cognitive Science come from a wide range of backgrounds
–including psychology, computer science, artificial intelligence,
philosophy, mathematics, neuroscience, and others – but they all share
the common goal of getting a better understanding of the human mind
through computational modelling. The developed models can roughly be
applied from two perspectives. Firstly, from a more theoretical
perspective, cognitive models (e.g., of perception, attention, or
decision making) can serve as a useful tool for researchers to gain more
insight in the dynamics of cognitive processes by building (and
simulating) them. Secondly, from a more practical perspective, cognitive
models can serve as a basis for the development of artefacts that either
show or understand human-like behaviour. Examples of artefacts that show
human- like behaviour are virtual characters in (serious) games, and
examples of artefacts that understand human-like behaviour are
intelligent support systems in cars or in military domains.

The programme consists of 120 ECTS.
Artificial Intelligence electives
Artificial Intelligence electives Deepening AI
Naam vak Periode Credits Code
Literature Study and Seminar Ac. Year (sept) 6EC X_405111
Mini-Master Project AI Ac. Year (sept) 6EC XM_400428
Advanced Machine Learning P1 6EC XM_0010
Intelligent Interactive Systems P1 6EC XMU_418023
Knowledge and Media P1 6EC X_405065
Behaviour Dynamics in Social Networks P2 6EC X_400113
Knowledge Engineering P2 6EC X_405099
Planning and Reinforcement Learning P2 6EC XM_0055
Learning Machines P3 6EC XM_0061
Advanced Logic P4 6EC X_405048
Seminar Combining Symbolic and Statistical Methods in AI P4 6EC XMU_0027
The Social Web P4 6EC X_405086
Data Mining Techniques P5 6EC X_400108
Entrepreneurship for AI and Computer Science P5 6EC XM_0009
Knowledge Representation on the Web P5 6EC XM_0060
ICT4D in the Field P6 6EC XM_0008
Machine Learning for the Quantified Self P6 6EC XM_40012