Machine Learning
This web page gives information on the lecture 'Machine Learning' which is held during winter term 2015/2016 by Andreas Nürnberger. It will be constantly updated during the course.
The course 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
Course Schedule and Room Assignments
| Time | Start | Room | |
| Lecture | Thursday 3:15 - 4:45pm | 15.10.2015 | G02-210 |
| Exercises | Wednesday 07:15 - 08:45am | 21.10.2015 | G29-K058 |
Course 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 exercise classes. Every 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. There are two different types of assignments: questions of understanding and programming assignments. The programming assignments can be solved in small groups of up to three students and must be sent in before the respective class. Prerequisites for an oral exam and a 'Schein' is fulfillment of the following criteria:
- Gaining at least 1/2 of all programming points
- 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.
Materials
Lecture Slides
Assignment Sheets
Other Resources
Literature
- Machine Learning
Tom Mitchell
McGraw-Hill, 1997. - Artificial Intelligence: A Modern Approach
S. Russel und P. Norvig
Prentice Hall, Englewood Cliffs, 2003