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Pattern recognition
Cursus informatie
Studiepunten (EC)7,5
After completing this course, the student:
  • knows important methods of statistical pattern recognition and their theoretical foundation
  • knows general principles of statistical learning, such as over-fitting and the bias-variance decomposition
  • knows how to apply methods of statistical pattern recognition to practical data analysis problems
  • can perform an experimental evaluation of statistical learning methods in a sound manner
  • knows the fundamental deep learning topics
  • has practical skills for applying deep learning methods (e.g. feed forward neural networks, CNNs, RNNs)
  • can model a research-oriented problem in the application domain of deep learning and suggest a solution using deep learning techniques

The course is graded through a practical assignment (20% of the final mark), a group project (40%) and a written exam (40%).

A repair test requires at least a 4 for the original test.

In this course we study statistical pattern recognition and machine learning.

The subjects covered are:

  • General principles of data analysis: overfitting, bias-variance trade-off, model selection, regularization, the curse of dimensionality.
  • Linear statistical models for regression and classification.
  • Clustering and unsupervised learning.
  • Support vector machines.
  • Neural networks and deep learning.

Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.

Course form
Lectures and computer lab sessions.


  • Book: Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  • Book: Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
    Book URL:
  • Possibly additional literature in the form of research papers, book chapters, etcetera.
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