Advanced Topics in Machine Learning

This web page provides information on the course Advanced Topics in Machine Learning (summer term 2016). 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 practical exercises.

Course Schedule and Room Assignments

Title Time Start Room
Lecture Thursday 3:00pm - 5:00pm 07.04.2016 G22A-209
Exercises Monday 09:00am - 11:00am 11.04.2016 G29-K058 G29-335

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):

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.

Lecture Slides
Exercise Material
Further Material

Last Modification: 01.07.2016 - Contact Person: