Advanced Topics in Machine Learning

General Course Information

On this web page, information on the course 'Advanced Topics in Machine Learning' (winter term 2008/2009) is provided. This course deals with selected advanced topics of Machine Learning. This includes:

  • SVMs
  • Semi-supervised learning (semi-supervised classification and clustering)
  • Dealing with massive datasets

Prerequisites for attending this course is basic knowledge of computer science and especially in Machine Learning. Programming skills are an advantage concerning the pratical exercises.

Course Schedule and Room Assignments

Title Time Start Room
Lecture Tuesday 3:00pm - 5:00pm 21.10.2008 G22A-208
Exercises Thursday 9:00am - 11:00am 23.10.2008 G29-144

Course Staff

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

Exercise Classes

The exercise classes have two objectives. First, assignments concerning the theory taught in the lecture will be given (about one week in advance). These have to be prepared by the students and are then discussed during class. Secondly, the lecture will be accompanied by a software project, in which we try to implement some ideas into a larger system. This will be done as a joint group work. The development will partly be done during the exercise classes. However, further development outside the class might be necessary to complete the project. We expect active involvement of all students, both in the project and the theoretical assignments.

Requirements for Class Fulfillment

At the end of the course, there will be an oral exam. As a prerequisite, we expect active involvement in the exercise classes.


We will provide lecture slides, assignment sheets, and further material during the course.

Lecture Slides

Exercise Material

Further Material

Last Modification: 12.04.2012 - Contact Person:

Sie können eine Nachricht versenden an: Prof. Dr.-Ing. Andreas Nürnberger