Advanced Topics in Machine Learning
On this web page, information on the course 'Advanced Topics in Machine Learning' (summer term 2010) is provided. This course deals with selected advanced topics of Machine Learning. This includes:
- 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.
|Lecture||Thursday 3:00pm - 5:00pm||1.04.2010||G22A-110|
Wednesday 1:00pm - 3:00pm
Monday 11:00am - 1:00pm
If you have any questions concerning the lectures or assignments please contact (if possible by email)
- Andreas Nürnberger
- Gerhard Gossen
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.
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.
- Course Info
- Computational Learning Theory
- Constrained Clustering
- Semi-Supervised Learning (Part 1)
- Semi-Supervised Learning (Part 2)
- Markov Models
- Massive Datasets
- Genetic Algorithms
- Assignment Sheet 1 (for 12.04.)
- Assignment Sheet 2 (for 19.04.)
- Assignment Sheet 3 (for 26.04.)
- Assignment Sheet 4 (for 03.05.)
- Assignment Sheet 5 (for 10.05.)
- Assignment Sheet 6 (for 17.05.)
- Assignment Sheet 7 (for 27.05.)
- Assignment Sheet 8 (for 07.06.)
- Assignment Sheet 9 (for 14.06.)
- Assignment Sheet 10 (for 21.06.)
- Assignment Sheet 11 (for 28.06.)