This course is intended to give a broad introduction to machine learning.
Also see the wikipedia entry here.
We will also devote some time on recent applications of machine learning.
Basic knowledge in algorithms and data structures and a not too strong aversion to maths.
Lectures: Friday 11:00-13:00 c.t. in room 024, MPI
First Lecture: Friday, April 20th
[Slides Part 1 (by Holger)]
[Slides Part 2 (by Jochen)]
Tutorials: Friday 15:00 c.t. in room 024, MPI
Lecture Notes Joachim Giesen: Lectures on linear maxium margin classifiers (lecture 2-4). Course material: scanned hand written notes and old notes from ETH Zürich.
Lecture Notes Stefan Funke:
Some notes on VC dimension can be found here, some on Kernels here, and some on Regression there.
Lecture Notes Holger Bast:
[Slides Lecture 8 (EM algorithm introduction)]
[Slides Lecture 9 (EM algorithm + convergence)]
[Slides Lecture 10 (Everything you alwyas wanted to know about statistics ...]
Wiki: Course Wiki (for question, comments, and uploading solutions to exercise sheets)
Exam: All information and your questions/comments on a separate Wiki page
- Dr. Holger Bast, Building 46, R.320,Homepage
- Dr. Kevin Chang, Building 46, R.313,Homepage
- Dr. Stefan Funke, Building 46, R.308,Homepage
- Dr. Joachim Giesen, Building 46, R.305,Homepage
To get a (graded) 'Schein' (6 CP) we expect the students to regularly attend
the lectures and tutorial sessions, submit solutions for the
handed-out exercise sheets, and pass the final exam at the end of the
semester. The final exam determines the grade of the 'Schein'.
Poll for dates of the tutorial here.