Advanced Topics in Machine Learning
This web page provides information on the course Advanced Topics in Machine Learning (summer term 2015). 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.
|Lecture||Thursday 3:00pm - 5:00pm||09.04.2015||G22A-209|
|Exercises||Monday 09:00am - 11:00am||13.04.2015||G29-K058|
Further information on the lecture and the exercise can be found in the LSF portal.
If you have any questions concerning the lectures or assignments please contact (preferably by email):
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.
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.
We will provide lecture slides, assignment sheets, and further material during the course.
- Course Information
- Computational Learning Theory
- Support Vector Machines
- Semi-Supervised Learning
- Constrained Clustering
- Markov Models
- Massive Datasets
- Genetic Algorithms
- Curse of Dimensionality
- Assignment Sheet 1 due by 13. April
- Assignment Sheet 2 due by 20. April
- Assignment Sheet 3 due by 27. April
- Assignment Sheet 4 due by 04. Mai
- Assignment Sheet 5 due by 11. Mai
- Assignment Sheet 6 due by 18. Mai
- Assignment Sheet 7 due by 01. June
- Assignment Sheet 8 due by 08. June
- Assignment Sheet 9 due by 15. June
- Assignment Sheet 10 due by 22. June
- Assignment Sheet 11 due by 29. June
- Assignment Sheet 12 due by 06. July
- Project assignment due by 15. June (for the students who want to participate in the challenge)
- Project assignment (Diabetes dataset) due by 15. June