De elektronische studiegids voor het academiejaar 2025 - 2026 is onder voorbehoud.





Advanced Topics in Data Science (4569)
  
Coordinating lecturer :Prof. dr. Fred VERMOLEN 
  
Member of the teaching team :Prof. dr. Frank NEVEN 
 Prof. dr. Geert Jan BEX 
 Prof. dr. Inneke VAN NIEUWENHUYSE 
 Prof. dr. Stijn VANSUMMEREN 

Language of instruction : English
Credits: 3,0
  
Period: quarter 3 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Prerequisites

The student has basic knowledge in programming.

The student has basic knowledge of linear algebra such as eigenvalue problems and singular value decomposition


Content

Five different topics by five different professors: Distributed Data Analysis, Frequent Itemset Handling, Gaussian Processes and Bayesian Optimization, Dimensionality Reduction, Numerical Linear Algebra.

Each of these topics will have a task exam and a lab work, which will have an equal weight. In order to pass the course, it is necessary that a minimum of 5/20 is obtained for each topic. A short oral assessment will be part of the evaluation.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Response lecture ✔  
Self-study assignment ✔  
Teaching methods  
Exercises ✔  
Homework ✔  


Evaluation

Period 2    Credits 3,00

Evaluation method
Written evaluaton during teaching periode75 %
Homework✔
Oral exam25 %
Open questions✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Different texts from the various modules. 
 

Recommended course material
 

Course material will be provided via blackboard


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Data Science J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.