Biography
Pieter Schoonees is a lecturer and academic director at the Econometric Institute of the Erasmus School of Economics. His research focuses on developing statistical and machine learning algorithms for dimension reduction and cluster analysis.
More information
Work
- Pieter C. Schoonees, Patrick J.F. Groenen & Michel van de Velden (2021) - Least-squares bilinear clustering of three-way data - Advances in Data Analysis and Classification, 1001-1037 - doi: 10.1007/s11634-021-00475-2 - [link]
- Hester van Herk, Pieter Schoonees, Patrick Groenen & Joost van Rosmalen (2018) - Competing for the same value segments? Insight into the volatile Dutch political landscape - PLoS One (online), 13 (1) - doi: 10.1371/journal.pone.0190598 - [link]
- Pieter Schoonees, Niel Roux & RLJ Coetzer (2016) - Flexible Graphical Assessment of Experimental Designs in R: The vdg Package - Journal of Statistical Software, 74 (3), 1-22 - doi: 10.18637/jss.v074.i03 - [link]
- Pieter Schoonees (2015) - Methods for Modelling Response Styles - [link]
- Pieter Schoonees (2015) - cds: Constrained Dual Scaling for Detecting Response Styles
- H (Hester) van Herk, Pieter Schoonees, Patrick Groenen & JM (Joost) van Rosmalen (2015) - Competing for the Same Value Segments: Explaining the Volatile Dutch Political Landscape - [link]
- Pieter Schoonees, Michel van de Velden & Patrick Groenen (2015) - Constrained Dual Scaling for Detecting Response Styles in Categorical Data - Psychometrika, 80 (4), 968-994 - doi: 10.1007/s11336-015-9458-9 - [link]
- Pieter Schoonees, Patrick Groenen & Michel van de Velden (2015) - Least-squares Bilinear Clustering of Three-way Data
- Pieter Schoonees (2015) - lsbclust: Least-Squares Bilinear Clustering for Three-Way Data
- Pieter Schoonees (2014) - vdg: Variance Dispersion Graphs and Fraction of Design Space Plots
Erasmus Quantitative Intelligence
- Start date approval
- October 2022
- End date approval
- October 2025
- Place
- ROTTERDAM
- Description
- Teaching in the post-master programme
Machine Learning I
- Level
- Master
- Year Level
- Master
- Year
- 2024
- Course Code
- TIC10200
Machine Learning I
- Level
- Master
- Year Level
- Master
- Year
- 2024
- Course Code
- TIF20200
Machine Learning II
- Level
- Master
- Year Level
- Master
- Year
- 2024
- Course Code
- TIC10203
Machine Learning II
- Level
- Master
- Year Level
- Master
- Year
- 2024
- Course Code
- TIF20203
Machine Learning 1
- Level
- Master
- Year Level
- Master
- Year
- 2024
- Course Code
- EBDS20102
