3 ECTS credits
80 h study time
Offer 1 with catalog number 1024262ENR for all students in the 1st semester
at
a (E) Master - advanced level.
- Semester
- 1st semester
- Enrollment based on exam contract
- Impossible
- Grading method
- Grading (scale from 0 to 20)
- Can retake in second session
- Yes
- Enrollment Requirements
- Students must have followed ‘Machine Learning’, before they can enroll for ‘Neural Networks'.
- Taught in
- Dutch
- Faculty
- Faculty of Sciences and Bioengineering Sciences
- Department
- Computer Science
- Educational team
- Lynn Houthuys
(course titular)
- Activities and contact hours
- 16 contact hours Lecture
16 contact hours Seminar, Exercises or Practicals
- Course Content
This course covers neural networks in an in-depth way. It provides an overview of the different approaches to neural networks and will then zoom in on the currently dominant form: deep neural networks. Because this is currently a very rapidly changing field, special attention will be paid to how students can keep their knowledge up to date.
- Additional info
n.a.
- Learning Outcomes
-
General competencies
- Knowledge and understanding of the 8 basic areas of AI: Algorithmic Problem Solving, Cognitive Science, Computational Linguistics, Context of AI, Intelligent Autonomous Agents, Interaction, Knowledge Representation and Machine Learning and the supporting modules: mathematics, computer science, logic and academic skills.
- In-depth knowledge and understanding of 3 of the basic areas for connection to master's level
- Being able to formulate, apply and validate AI models.
- Being able to independently update the knowledge acquired and to tackle new problems in science and applications. Be aware of the need to constantly update knowledge.
- Grading
-
The final grade is composed based on the following categories:
Written Exam determines 60% of the final mark.
PRAC Practical Assignment determines 40% of the final mark.
Within the Written Exam category, the following assignments need to be completed:
- Written Exam
with a relative weight of 1
which comprises 60% of the final mark.
Within the PRAC Practical Assignment category, the following assignments need to be completed:
- Practical Assignment
with a relative weight of 1
which comprises 40% of the final mark.
- Additional info regarding evaluation
During the semester a project (Python code + report) has to be done (40% of the final grade). During the exam period a written closed book exam covering all the course topics will be conducted (60% of the final grade) as well as a discussion of the project.
In case of an overall failure, partial marks for the assignment, if the student obtains at least 10/20 for the assignment, are transferred to the second session. Partial marks for the written exam, if the student obtains at least 10/20 for the written exam, are transferred to the second session.
- Allowed unsatisfactory mark
- The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.
Academic context
This offer is part of the following study plans:
Bachelor of Artificial Intelligence: Default track (only offered in Dutch)