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 2018/2019 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 (subject to change):
- value functions
- concept spaces and concept learning
- instance based learning
- decision trees
- neural networks
- Bayesian learning
- reinforcement learning
- association rule learning
- genetic algorithms
Module Schedule and Room Assignments
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.
If you have any questions concerning the lectures or assignments please contact (if possible by email):
- Prof. Andreas Nürnberger
- Marcus Thiel
Requirements for the 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 the written exam is the 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 written. For the 'Schein', you also have to write the exam.
The paper deadline is the 7th of January 2019 (until midnight).
- Paper Template
- Examples for Topics + Requirements
- For seminar topics and guidance please contact:
- Machine Learning
- Artificial Intelligence: A Modern Approach
S. Russel und P. Norvig
Prentice Hall, Englewood Cliffs, 2003