6 ECTS credits
150 u studietijd
Aanbieding 1 met studiegidsnummer 4024145ENR voor alle studenten in het 1e en 2e semester met een verdiepend master niveau.
In this seminar the students get insights about recent developments in the field of subsymbolic AI, more specifically the field of kernel models. This is an advanced course about selected topics from the state of the art in kernel models and techniques, including multi-modality and deep learning.
The first part of the course will cover the principles of kernel methods, the support vector machine framework and kernel methods for various learning tasks.
The second part of the course will cover state of the art topics, such as multi-modality and deep learning, as well as aspects and discussions on fairness and explainability. The exact content of this part can vary each year, in order to reflect the latest research and practise. Students will have to study a certain topic in a small group, and present their findings to their fellow students.
During the practical sessions students will work on python exercises related to the material from the lectures. Additionally homework problems will be given which will count towards their final grade.
None
Knowledge and Insight:
The student can explain the working of kernel methods in general, and the working of the covered kernel methods in specific.
The student shows insight in the recent trends regarding kernel-based learning.
Application of Knowledge and Insight:
Given a concrete learning problem, the student selects an appropriate kernel-based method, applies it correctly and is able to correctly analyse the performance.
Judgement Shaping:
The student collects and interprets literature about subsymbolic AI, and is able to understand it to a sufficient level in order to apply it correctly in appropriate problems.
Communication:
The student communicates about kernel-based problems and can report and present the results of their experiments, with both experts and non-experts.
Learning Skills:
After having attended this course, the student has the necessary knowledge to independently investigate a given research topic based on specific research papers and other resources.
De beoordeling bestaat uit volgende opdrachtcategorieën:
Examen Andere bepaalt 100% van het eindcijfer
Binnen de categorie Examen Andere dient men volgende opdrachten af te werken:
The final grade consist of three parts:
Deze aanbieding maakt deel uit van de volgende studieplannen:
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Artificiële Intelligentie
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Multimedia
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Software Languages and Software Engineering
Master in de ingenieurswetenschappen: computerwetenschappen: afstudeerrichting Data Management en Analytics
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (enkel aangeboden in het Engels)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (enkel aangeboden in het Engels)