This course aims to
- familiarize the student with current research issues in Artificial Intelligence (e.g., as reported in journals like Artificial Intelligence, Journal of Experimental and Theoretical Artificial Intelligence, Pattern Recognition, Trends in Cognitive Science, Neural Networks, Connection Science)
- promote a critical reflection on such issues,
- stimulate the student to consider potentially interesting master thesis projects.
A number of current, 'hot', research themes from the field of Artificial Intelligence (AI) will be addressed, both in the form of presentations giving a general overview, and through specific research papers. The themes will be diverse, each theme will be covered by a different AI-researcher who is a specialist in the field. Whenever possible, opposite or complementary views on the topic will be presented.
In total, 9 themes will be covered (which may vary per year). Each theme will be divided over two consecutive meetings. In the first meeting, an AI-researcher will present an introduction to the theme. For this first part, typically review articles from leading journals will be assigned as reading material. In the next meeting, one to three student groups will present a talk on the basis of an article from the primary literature, in which they will give a critical reflection on that article. These articles are suggested by the lecturer responsible for the theme.
Besides the scheduled meetings, each student is required to obtain 8 “colloquium points” by attending, and writing a short summary of scientific presentations or labmeetings outside the course meetings (e.g. Donders lectures
). More details can be found in the course guide on Brightspace.