Machine Learning
On this web page, information for the course 'Machine Learning', which is held during winter term 2009/2010 by Andreas Nürnberger, is given. This page will be constantly updated during the course.
Allgemeines
This 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 | Tuesday 3:00 - 5:00pm | 12.10.2009 | G22A-208 |
| Exercises | Monday 1:00 - 3:00pm | 19.10.2009 | G29-K058 |
Course Staff
If you have any questions concerning the lectures or assignments please contact (if possible by email):
- Prof. Andreas Nürnberger
E-Mail: andreas.nuernberger@ovgu.de - Dr.-Ing. Korinna Bade
E-Mail: korinna.bade@ovgu.de
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 (these are assigned based on difficulty of the individual tasks)
- 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
- Introduction
- Concept Learning and Version Space
- Decision Tree Learning
- Neural Networks
- Bayesian Learning
- Instance-based Learning (extended and slides 8 and 12 corrected, 9.12.2009)
- Reinforcement Learning
- Association Rule Learning (updated 20.1.2010)
- Genetic Algorithms (updated 27.1.2010)
Assignment Sheets
- Scoring of programming assignments
- Example of an ant script
- Assignment sheet 1
TicTacToe Environment - Assignment sheet 2
- Assignment sheet 3
Car Data - Assignment sheet 4
- Assignment sheet 5
- Assignment sheet 6
- Assignment sheet 7
- Assignment sheet 8
- Assignment sheet 9
- Assignment sheet 10
- Assignment sheet 11
- Assignment sheet 12
- Assignment sheet 13
Literatur
- Machine Learning
Tom Mitchell
McGraw-Hill, 1997. - Artificial Intelligence: A Modern Approach
S. Russel und P. Norvig
Prentice Hall, Englewood Cliffs, 2003