Introduction to Pattern Recognition (code: NB054B)

This course is replaced by Statistical Machine Learning

for students AI and computer science (and others who are interested)

In the field of (statistical) pattern recognition the aim is to learn a computer (by examples) to recognize patterns in data sets (e.g. input-output relations). Real data is often noisy, and therefore probabilistic methods are used. In this course we take the Bayesian perspective, which will be the starting point for a treatment of both classical methods (least mean squares methods, discriminant analysis) and modern methods (neural networks, Bayesian learning).

This course aims to a principled treatment of pattern recognition. For a good understanding of pattern recognition (as well as of many other subjects in modern AI), a certain mathematical depth is necessary. In this course, we will not avoid the mathematics. However, ample time will be reserved to acquire the necessary mathematical knowledge and skills.

Course information

Course scheme

We start February 3. Other information is to be provided in blackboard, or will be discussed during the course.

Additional course material (2010)


Web solutions !!

Solutions of all www exercises from the first four book chapters. Available from the author's home page: pdf


Other material is to be provided on blackboard.