Machine Learning for Medical Systems

This web page provides information on the module 'Machine Learning for Medical Systems'. The module consists of the lecture 'Machine Learning', which is given during winter term 2016/2017 by Andreas Nürnberger, and the seminar 'Machine Learning for Medical Systems', which will take place every week. The time for the seminar will be coordinated at the first lecture date. For additional information on the course and the exercise (with regular updates), please visit the Machine Learning website.

The module provides an introduction to the principles, techniques, and applications of Machine Learning. Topics covered include among others:

  • value functions
  • concept spaces and concept learning
  • instance based learning
  • clustering
  • decision trees
  • neural networks
  • Bayesian learning
  • reinforcement learning
  • association rule learning
  • genetic algorithms

Module Schedule and Room Assignments

  Time Start Room
Lecture Tuesday 3:15 - 4:45pm 11.10.2016 G22A-211 G02-109
Seminar Monday 3:15 - 4:45pm 17.10.2016 G22A-120

There is NO extra seminar date! Please visit the regular exercise!


Registration for the exercise is done separately in each exercise group. Please visit the Machine Learning page for more information.

Module Staff

If you have any questions concerning the lectures or assignments please contact (if possible by email):

Requirements for the Oral Exam and the 'Schein'

All students are required to participate in the seminar classes. Each week, there will be an assignment sheet that will be handed out one week in advance. This sheet has to be prepared by every student and will be discussed in class. Prerequisites for an oral exam and a 'Schein' is fulfillment of the following criteria:

  • Solving at least 2/3 of all questions of understanding
  • Presenting at least 2 solutions in class.

The exam will be oral. For the 'Schein', there will be also an oral colloquium of about 10 minutes.


Lecture Slides
  • ...
Assignment Sheets


Last Modification: 18.10.2016 - Contact Person: