Required in stage
Optional in stage
First term
Both terms
Not organised
This year
Next year
Alternating years
External
Prerequisites
Identical coursesIntroduction to Artificial Intelligence Technologies (B-KUL-HNI44A)
Cannot be taken as part of an examination contract
De Mulder Wim (coordinator) | N. | De Mulder Wim (substitute) | Koolen Christof (substitute) Aims
Upon completion of this course, students understand the basic elements of artificial intelligence (AI) technologies, such as NLP, computer vision, machine learning, deep learning, etc., that are essential for lawyers dealing with AI governance and regulation. They are able to use these insights in a legal analysis of AI related problems. (Competence level 3).
This course contributes to the following learning outcomes:
1. The student masters the structure and methodology of the international and European and / or Belgian intellectual property rights, media and / or ICT law that he / she has followed.
2. The student can deal with complex problems in the domain of the intellectual property rights, media and / or ICT law that he / she has followed.
4. The student can adopt a critical position in relation to the domain of the intellectual property law rights, media law and / or IT law that he / she has followed.
Previous knowledge
None
Is included in these courses of study
Activities
3 ects. Introduction to Artificial Intelligence Technologies (B-KUL-HNI44a)
EnglishFormat: Lecture
18 
Second termN. | De Mulder Wim (substitute) | Koolen Christof (substitute) Content
This subject introduces students to the key elements of artificial intelligence technologies. It starts with an introduction into AI, its history, definition and the so-called ‘AI effect’. It clarifies machine learning, and its cultures, i.e. Deep (neural networks), Bayesian, and Symbolic. It explains (un-)supervised learning and the pivotal role of datasets. Subsequently, it expands on reasoning. It concludes with an outlook on the future of AI. Students will also take part in practical assignments, such as building simple machine learning pipelines to learn patterns in data, e.g. using RapidMiner.
Course material
List of course materials provided through Toledo.
Language of instruction: more information
This subject is an elective in an international advanced master’s programme.
Format: more information
The subject is taught in the form of lectures with interactive elements and assignments during the classes.
The lectures will be given on campus and will, in addition and subject to technical feasibility, be streamed online and recorded. Lecture recordings will be made available for a period of 3 weeks after each lecture. Lecture recordings will not be made available again during the exam period(s).
Evaluation
Evaluation: Introduction to Artificial Intelligence Technologies (B-KUL-H90026)
Explanation
Throughout the semester, students will be evaluated on the basis of assignments (in class and/or take-home) (25%).
The written exam at the end of the semester consists of 5 open questions (evaluating students’ reproduction and application skills) (75%).