# Machine Learning

Summer 07

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.
### Prerequisites

Basic knowledge in algorithms and data structures and a not too strong aversion to maths.
### Further Information

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

Language: English

**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

#### Lecturers

- 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

#### Grading

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'.
### Exercises

Poll for dates of the tutorial here.