Required in stage
Optional in stage
First term
Second term
Not organised
This year
Next year
Alternating years
External
Prerequisites
Identical coursesApplied AI: Academic Perspectives (Two Modules) (B-KUL-B30773)
Cannot be taken as part of an examination contract
De Lathauwer Lieven (coordinator) | De Lathauwer Lieven | Gryllias Konstantinos | Karsmakers Peter | Vanrumste Bart | Vennekens Joost | Van Schoors Rani (cooperator) | Winters Thomas (cooperator) | LessMoreAims
Learning outcomes
MMK1 Scientific-disciplinary knowledge and comprehension in the field of Artificial Intelligence
MMK2 Gaining in-depth knowledge and comprehension, including the ability to develop relevant prototypes or proof-of-concepts, in at least one of the following disciplines in Artificial Intelligence: machine learning, deep learning, knowledge representation, computer vision, audio processing, natural language processing, search and optimisation. Gaining in-depth knowledge and comprehension of at least one of the following application domains of Artificial Intelligence: health, education, logistics, manufacturing, robotics.
MMG3 Critical thinking
MMI2 Design and/or development
MMI3 Application-oriented research
MMG4 Working in a team in different roles
MMG5 Professionalism
Objectives
The student acquires more in-depth knowledge about particular AI technologies and also a deeper understanding of how AI techniques are used in practice to solve concrete problems. With respect to the latter, the student learns:
- to identify situations in which AI can create added value,
- to select a suitable AI method,
- how to develop an AI solution,
- how to evaluate whether an AI solution has achieved its goal.
The student chooses to study these issues from certain specific perspectives, by selecting two of the following modules:
- Applied Knowledge Representation
- Combinatorial optimisation for industry
- AI & Education
- AI & Tensors
- AI & Health
- Machine Listening
- AI for Maintenance and Condition Monitoring
- Generative AI
The above list is subject to change in case of unexpected circumstances. If no more than three students register for a specific module, it may not be organised and students may be asked to choose a different module.
The modules focus on the academic and technological aspects of the AI methods used:
- The student learns how different AI technologies are typically used in different domains, how these technologies works, and what is and is not possible in the current state-of-the-art.
- The student learns to formulate relevant research questions in these domains and to come up with an experimental setup to address these research questions.
Previous knowledge
The student has basic knowledge of computer programming and of fundamental AI methods, such as taught in the course Fundamentals of AI.
Order of Enrolment
Mixed prerequisite:
You may only take this course if you comply with the prerequisites. Prerequisites can be strict or flexible, or can imply simultaneity. A degree level can be also be a prerequisite.
Explanation:
STRICT: You may only take this course if you have passed or applied tolerance for the courses for which this condition is set.
FLEXIBLE: You may only take this course if you have previously taken the courses for which this condition is set.
SIMULTANEOUS: You may only take this course if you also take the courses for which this condition is set (or have taken them previously).
DEGREE: You may only take this course if you have obtained this degree level.
SIMULTANEOUS(H0Q38A)
The codes of the course units mentioned above correspond to the following course descriptions:
H0Q38A : Fundamentals of Artificial Intelligence
Is included in these courses of study
Activities
4 ects. Applied AI: Academic Perspectives (Two Modules) (B-KUL-B5517T)
EnglishFormat: Lecture
24 
Both termsDe Lathauwer Lieven | Gryllias Konstantinos | Karsmakers Peter | Vanrumste Bart | Vennekens Joost | Van Schoors Rani (cooperator) | Winters Thomas (cooperator) | LessMoreContent
This course has a modular structure, in which each module covers one specific AI topic. At the start of the course, students register to follow two modules. Students are only expected to attend the lectures for those modules for which they register. (If no more than three students register for a module, it may not be organised and students may be asked to choose a different module.)
Detailed information about the timetabling of the lectures is available on Toledo.
The lectures of a module may be interactive, requiring the student to actively participate by, e.g., working on a project during the lectures. In such case, attending the lectures may be mandatory. It will be clearly indicated on Toledo whether this is the case. Some modules may implement a blended learning approach, in which a number of lectures are replaced by material (typically knowledge clips) that the student processes off-campus.
Is also included in other courses
Evaluation
Evaluation: Applied AI: Academic Perspectives (Two Modules) (B-KUL-B78937)
Explanation
The students are evaluated based on two papers/projects:
1) For one of the modules, the student participates in a group project. In this project, the students independently execute an AI project in the domain of the module. They write a paper detailing their approach and evaluating their work. In an appendix to this paper, they provide all information (e.g., produced code) needed to be able to reproduce their experiments.
2) For the other module, the student critically reviews one of the projects made by another group of students. The student writes a review in which he/she comments on the chosen approach, discusses possible alternatives and how he/she would rate the results of the project.
The student’s own project makes up 70% of the grade, while the review makes up 30%. Peer review is used to differentiate individual students w.r.t. the group score.
Information about retaking exams
No 2nd examination opportunity.
It is not possible to resit this exam.
The learning objectives of this course are realized and evaluated by means of a project, which is executed iteratively and based on regular feedback from the professor. It is impossible to conduct an equivalent evaluation in a resit.