22C3 - 2.2

22nd Chaos Communication Congress
Private Investigations

Timon Schroeter
Sören Sonnenburg
Konrad Rieck
Day 1
Room Saal 2
Start time 21:00
Duration 01:00
ID 544
Event type Lecture
Track Science
Language English

Applied Machine Learning

Brief Introduction into Machine Learning followed by application examples.

Overview of the current state of research in Machine Learning including the general motivation, setup of learning problems, state-of-the-art learning algorithms and applications like our brain computer interface.

The talk is going to have three parts: (a) What is Machine Learning about? This includes the general motivation (spam detection as example) and the setup of supervised learning problems. (b) What are state-of-the-art learning techniques? With a minimal amount of theory, I'll describe some methods including a currently very successful and easily applicable method called Support Vector Machines. I'll provide references to packaged implementations of these algorithms. (c) I'll discuss a few applications in greater detail, to show how Machine Learning can be successfully applied in practice. These will include: Handwritten letter/digit recognition, drug discovery, file classification (e.g. on Linux and BSD sourcecode), gene finding and brain-computer interfacing. I present the material as self-contained as possible. Part b will contain some math, but this will be kept to a minimum: I mainly want to bring ideas across.