Machine Learning: Supervised Methods

The field of machine learning constitutes a modern approach to artificial intelligence. It is situated in between neuroscience, statistics, robotics, and areas of application ranging all over science and engineering, medicine, economics, and many more. Machine learning algorithms automate the process of learning, thus allowing prediction and decision making machines to improve with experience.

This lecture will cover different state-of-the-art methods in the domain of "supervised learning". Topics include classical statistical methods, neural networks, support vector machines, and nearest neighbor models. The lecture covers algorithmic as well as learning theoretical aspects.

The 2 hours/week lecture is accompanied by a 2 hours/week practical course. It will be held either in German or in English, depending on the audience. Most of the course material will be in English.

Lecturers

Details

Course type
Lectures
Credits
6 CP
Term
Summer Term 2016

Dates

Lecture
Takes place every week on Monday from 10:00 to 12:00 in room ND 6/99.
First appointment is on 11.04.2016
Exercise
Takes place every week on Thursday from 12:00 to 14:00 in room NB 3/72.
First appointment is on 14.04.2016

Requirements

The course is designed for Master students of the Angewandte Informatik program. The lecture "Mathematics for Modeling and Data Analysis" is recommended as a background.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210