Instructors: prof. Bert de Vries, dr. Tjalling J. Tjalkens, and ir. Marco Cox

In this course, using fundamental concepts of probability theory, we present an introduction to the design of adaptive information processing systems. This course extends coursework on adaptive signal processing and can also be taken as an introduction to machine learning and data science. Typical application areas include pattern recognition, medical signal analysis, speech and language processing, image processing, bio-informatics and robotics.

In the 2016/17 academic year, this class is taught in semester B (3rd quarter) and starts on 6-Feb-2017. Please check the official TUE course site for more detailed information on meeting times and location.


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Part 1: Linear Gaussian Models and the EM Algorithm

Part 2: Model Complexity Control and the MDL Principle

Exam Preparation


The 2007 class meetings were recorded and can be viewed if you have a valid TU/e account. Note however that the current class will change a bit relative to the 2007 class. Talk to us before you plan to follow the class only from video.


  • Prerequisites: Mathematical maturity equivalent to undergraduate engineering program. Some MATLAB programming skills are helpful.

  • This course replaces the 3-ECTS course 5MB20-Adaptive Information Processing, which was taught between 2005-2014. The new course 5SSB0 is a 5-ECTS course and while the contents are similar to 5MB20, some lessons have been extended with new materials. The slide materials for 5MB20 for the academic year 2014/15 are still available here.

  • You’re advised to bring the lecture notes (either in soft- or hardcopy) with you to class in order to add your personal comments.

  • Some related resources on the net with lots of relevant content