3 ECTS credits
75 h study time

Offer 2 with catalog number 8024160GNR for all students in the 2nd semester at a (G) Postgraduate - preliminary level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Hugues Bersini
Johan Loeckx (course titular)
Activities and contact hours
16 contact hours Lecture
19 contact hours Seminar, Exercises or Practicals
Course Content

Module 2 examines the technical and data aspects of AI: how does it work? What are the different subdomains? What data should I collect? How to organise a proper data governance? What is the relation between software and hardware in AI? What is the impact on my technology stack? Who are the (cloud) service providers and what is the role of open-Source software?  

 
Additional info

 

 
Learning Outcomes

General Competences

  1. The student understands, can explain and apply – at a conceptual level – the basic methodologies behind data science and the principles and approaches to machine learning. 
  2. The student understands the challenges connected to handling data and extracting value, and how it can lead to ethical problems, including fairness. 
  3. The student understands the organisational challenges connected to storing, managing and governing data. 
  4. The student grasps potential how new kinds of security and robustness problems can be introduced due to the use of AI technology. 
  5. The student has a high-level understanding of the new technological stack and software development ecosystem, including open-source software and cloud computing. 
  6. The student understands the main challenges in robotics, and how robotics intersects with AI. 
 

Grading

The final grade is composed based on the following categories:
Written Exam determines 100% 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 100% of the final mark.

Additional info regarding evaluation

  

 
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:
Postgraduate Certificate AI for the Common Good: Default track