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Home | Business Data Science Courses in 2020 | Supervised Machine Learning

Supervised Machine Learning

Registration for this course is closed.

Course is scheduled in the period January 7 - February 28. 
Course locations: Erasmus University Rotterdam campus. 

Supervised Machine Learning

Registration for this course

Registration for this course is closed. Max. capacity: 25 students. 

Course Schedule

Courses are scheduled twice a week in the period January 6 - February 14 on Tuesday afternoon, Wednesday afternoon (until January 23) and on Friday morning (as of January 31). Exam will be scheduled in week 9. Tutorials to be scheduled. 

Course Content

Statistical learning methods arising from statistics, machine learning, and data science have become more widely available. These methods can be split into supervised learning, with the aim of predicting a response variable, and unsupervised learning, which aims to describe the relations between all variables simultaneously. This course focusses on supervised learning and has as its goal that the student obtains a thorough technical understanding of a selection of supervised machine learning techniques, can implement the technique in the high level language R and can write a report about an application of the technique.

The book of Hastie, Tibshirani, and Friedman (2001, 1st edition) has been a milestone in connecting statistical ideas into machine learning techniques. Parts of the second edition of this book (2009) form the basis of this course. An overview of techniques and ideas to be treated are:

  • linear methods for regression,
  • linear methods for classification,
  • basis expansion and regularization,
  • model assessment and selection,
  • classification and regression trees,
  • ensemble learning (random forests, bagging, and boosting),
  • support vector machines.

Learning Objectives

By the end of the course students will be able to:

  • Understand the fundamental building blocks of several supervised machine earning methods, with specific attention to: linear methods for regression and classification; basis expansion and regularization; model assessment and selection; classification and regression trees; ensemble learning (random forests, bagging, and boosting); support vector machines.
  • Program these methods by translating technical knowledge of a method into their own code in R.
  • Apply and interpret these methods.
  • Write a small report in the form of a short scientific article.


Coordinator/Lecturer: prof. dr. Patrick Groenen (EUR),
Lecturer: dr. Pieter Schoonees (EUR)

Course Fees

Course fee for internal research master and PhD students: € 1.000,-
Course fee for external PhD students: € 1.500,-

Entrance Requirements

Recommended knowledge: Business Foundations, Programming Basics (specifically programming in R, use of R Markdown or knitr), Mathematics, Statistics
Required knowledge: Linear Algebra

Link to course manual


Registration for this course

Registration for this course is closed.