De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Continuous Optimisation (9786)
Coordinating lecturer:Prof. dr. Inneke VAN NIEUWENHUYSE 
Member of the teaching team:dr. ing. Sasan AMINI 
 De heer Vipul CHALOTRA 

Credits: 3,0
Study load hours: 81
Period: semester 2 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
No sequentiality


Prerequisites

The student needs to have knwledge of Python and a decent background in basic statistics/statistical concepts.


Content

The course offers an introduction to data-efficient continuous optimization techniques (single objective, multi-objective, constrained). Contents: 1) Bayesian Optimization using Gaussian Processes; 2) Bayesian Optimization using Tree Parzen Estimators.


Compulsory course material
 

All materials will be distributed thorugh blackboard.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  
Small group session ✔  


Evaluation

Semester 2 (3,00sp)

Evaluation method
Written evaluation during teaching period50 %
Report✔
Oral exam50 %
Open questions✔
Presentation✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art design methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

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

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

     
  •  DC 
    		• The student is able to extract user tasks from domain experts.
  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

     
  •  DC 

    • The student is an effective oral communicator in their own field.

 
Included in these programmesTolerance3
Y



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.