Advanced Topics in Machine Learning

This web page provides information on the course Advanced Topics in Machine Learning (summer term 2014). The course deals with selected topics of Machine Learning, including:

Support Vector Machines
Semi-Supervised Learning (semi-supervised classification and clustering)
Dealing with massive datasets

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

Course Schedule and Room Assignments

Title	Time	Start	Room
Lecture	Thursday 5:00pm - 7:00pm	04.2014	G29-K058
Exercises	Monday 11:00am - 1:00pm	14.04.2014	G22A-122

Further information on the lecture and the exercise can be found in the LSF portal.

Course Staff

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

Andreas Nürnberger
email: andreas.nuernberger@ovgu.de
Tatiana Gossen
e-mail: tatiana.gossen@ovgu.de

Exercise Classes

The exercise classes have two objectives. First, regular 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. Its goal is to practice the implementation of machine learning techniques 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 both during the exercise and in the software project.

Materials

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