SIKS-course “Machine Learning and Optimisation”


On the Monday and Tuesday, 10-11 October 2022, the School for Information and Knowledge Systems (SIKS) organizes a new course on “Machine Learning and Optimisation”. This two-day course will take place in Utrecht. The course will be given in English and is part of the Educational Program for SIKS PhD candidates. It is a course with a broad focus, aimed at all SIKS PhD candidates regardless of the research area they are actually working on. Although this course is primarily intended for SIKS PhD candidates, other participants are not excluded. However, their admittance will be restricted and depends on the number of SIKS PhD candidates who wish to enroll.

Monday and Tuesday, 10-11 October 2022


Neil Yorke-Smith, TUD
Yingqian Zhang, TU/e


Monday Oct 10th
09.30 Introduction
09.45 Brief introduction to techniques of machine learning and optimization by Wouter Kool (UvA, Ortec)
10.15 break
10.45 Learning to solve routing problems by Wouter Kool (UvA, Ortec)
11.30 Lab 1: VRP competition Wouter Kool (UvA, Ortec)
12.30  lunch
14.00 DT learning by Sicco Verwer(TUD)
15.15 coffee
15.45 Lab 2: Learning decision trees by Sicco Verwer (TUD)
16.30 Application: analytics for a better world by Joaquim Gromicho (UvA, Ortec)
17.30 wrap

Evening activity

Tuesday Oct 11th

09.00 Surrogate-based optimization by Laurens Bliek (TU/e)
10.15  break
10.45 lab 3: SBO by Laurens Bliek (TU/e)
11.45 Application: data driven optimization, by Yingqian Zhang (TU/e)
12.30 lunch
14.00 Optimization techniques for ML  by Marleen Balvert (TiU)
15.15 coffee
15.45 lab 4: free choice
16.45 feedback
17.00 wrapping up

Participation is free For registration you are kindly requested to fill in the registration form

Deadline for registration for SIKS-Ph.D. Students: 01 October 2022

After that date, applications to participate will be honoured in a first-come first-serve manner. Of course, applications to participate from other interested groups are welcome already. They will receive a notification whether they can participate as soon as possible.

Information for non-SIKS Ph.D. Students
SIKS needs a confirmation from your supervisor/office that they agree with the arrangement and paying conditions.