Otto-von-Guericke-Universität Magdeburg
Hauptnavigation
Fußnavigation
Textanfang
Machine Learning
General Course Information
On this web page, information (slides, assignments, etc.) for the course 'Machine Learning', which is held during summer term 2008 by Andreas Nürnberger, is given.
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
- computational learning theory
- Bayesian learning
- reinforcement learning
Prerequisites for attending this course is basic knowledge of computer science. However, we are also open for interested students from other faculties.
Course Schedule and Room Assignments
| Title | Time | Start | Room |
| Lecture | Thursday 3:00pm - 5:00pm | 03.04.2008 | G22A-110 |
| Exercises | Wednesday 1:00pm - 3:00pm | 09.04.2008 | G29-K059 |
Course Staff
If you have any questions concerning the lectures or assignments please contact (if possible by email)
- Andreas Nürnberger
email: andreas.nuernberger@ovgu.de - Ernesto William De Luca
email: ernesto.deluca@ovgu.de
Requirements for the 'Schein'
For each exercise lesson assignments will be given (usually one week in advance). There will be two categories of assignments: Programming and others. Programming assignments can be done by a group of two or three students, all other assignments have to be done individually. To all assignments depending on its complexity a specific number of points are given (approx. 25% of the assignments will be programming). In the beginning of each exercise lesson, every student has to indicate the assignments that he has solved and that he is willing to present and discuss.
In order to obtain a 'Schein', the following criteria have to be fulfilled:
- at least half of the achievable points for the programming assignments,
- at least half of the achievable points for the other assignments,
- at least two presentations of solutions during the exercises and
- successful participation in a short colloquium (approx. 10 min.) after the end of the course
Requirements for the Oral Exam
We require all our students to participate in the excercise lessons. Students, who want to take an oral exam, are also supposed to fulfill the requirements for a 'Schein'. The colloquium will be replaced by the oral exam.
Materials
We will provide the lecture slides, assignment sheets, and further material during the course.
Lecture Slides
- Course Introduction
- Machine Learning Introduction (english) / Machine Learning Einfuehrung (german)
- Concept Learning (english) / Begriffslernen und Versionsraeume (german)
- Decision Tree Learning (english)
- Neural Networks (english) / Neural Network Simulation Tool (wmlp.exe)
- Bayesian Learning (english)
- Instance Based Learning (english)
- Clustering (english)
- Association Rule Learning (english) - Paper "Mining Generalized Association Rules" of Srikant and Agrawal, 1995.
- Reinforcement Learning (english)
Assignment Sheets
- Assignment 1
- TicTacToe.zip
- Assignment 2
- Assignment 3
- cardata.zip
- Assignment 4
- Assignment 5
- Assignment 6
- Assignment 7
- Assignment 8
Literature
- Machine Learning
Tom Mitchell
McGraw-Hill, 1997. - Artificial Intelligence: A Modern Approach
S. Russel und P. Norvig
Prentice Hall, Englewood Cliffs, 2003
Last Modification: 14.11.2011 - Contact Person: Prof. Dr.-Ing. Andreas Nürnberger