6 ECTS credits
150 h study time
Offer 1 with catalog number 4024145ENR for all students in the 1st and 2nd semester at a (E) Master - advanced level.
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.
The final grade is composed based on the following categories:
Other Exam determines 100% of the final mark.
Within the Other Exam category, the following assignments need to be completed:
The final grade consist of three parts:
This offer is part of the following study plans:
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (only offered in Dutch)
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics