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Research Seminar Artificial Intelligence


Admission requirements

Bachelor degree (completed)


This seminar-style course studies the topic of artificial intelligence, taking a broad and historical view. Presentations are mainly held by the students themselves, while some are given by the lecturer. Goal of the course is to learn studying, processing and presenting scientific material, and to learn about artificial intelligence. The seminar consists of lectures, multiple homework assignments/tests, and student presentations.

It covers various sexy topics from the field of artificial intelligence, to the level that should enable students to discuss AI comfortably with other scientists. The selected topics were chosen to be practically applicable for future Media Technology projects, or to make students think about future directions. They include the question of whether machines can think, evolutionary computation, neural networks, computing with DNA, computers and emotions, computational creativity and more. It is not a complete overview of AI topics. Some topics are not strictly AI but related; they were included to understand the history and broader context of artificial intelligence.

Course objectives

After succesfully completing the course, students

  • know the history and challenges of AI;

  • know the breadth of the field, and the different views on what AI is;

  • have basic knowledge and understanding of key concepts, such as philosophical concepts, the Turing Test, neural networks, evolutionary algorithms, DNA computing, natural language processing, affective computing, fear of AI, and more;

  • have some working knowledge of basic feedforward neural networks training and evolutionary algorithms;

  • can comfortably discuss AI within an academic environment;

  • could consider taking a more in-depth course on specific topics from this course.


Check MyTimetable (manual) and use your ULCN account to login. Please note that (last-minute) changes in the schedule are communicated in the course's Brightspace.

Mode of instruction

Lecture, Seminar

Assessment method

Student presentations and multiple written tests (in-class). Frequent (un-announced) written tests about the course material are part of the lectures. As such, attendance in all lectures is absolutely compulsory.

Reading list

No book, only web-available materials. Communicated via the course Brightspace.


  • You have to enrol for classes and exams (including retakes) in uSis.

  • Elective, external and exchange students (other than Media Technology students) need to contact the programme's coordinator due to limited capacity.


Contact the lecturer(s) for course specific questions and the programme's coordinator Barbara Visscher-van Grinsven for questions regarding the programme, admission and/or registration.