Institute for Numerical Simulation
Rheinische Friedrich-Wilhelms-Universität Bonn
maximize

Summer Semester 2018

P4E1 Practical Lab Numerical Simulation
Algorithms in Machine Learning and Their Application

Under direction of
Prof. Dr. Jochen Garcke
Prof. Dr. Michael Griebel
Place
Wegelerstr. 6, Room 6.020
Date
Wednesday, 14:15 - 16:00
Registration
mllab.ins.uni-bonn.de

We are testing the new website of the INS for this practical lab. All exercise sheets and further information will be published there. The address will be announced at the first meeting.

Registration

We have still places available. Please send an e-mail to the registration address above before the first meeting.

Background

Nowadays, data mining and machine learning algorithms are the backbone of decision making processes in all major enterprises. Their applicability seems almost endless and ranges from selective advertising over prototype design to autonomous production chains. Due to the availability of very large datasets (“Big Data”) it has become crucial to understand the mechanics of the different types of learning methods and to be able to develop and implement efficient algorithms to meet the requirements of the task at hand.

Content

In this practical lab, we teach the basic mathematical and technical tools needed to understand a range of basic data mining and machine learning methods. A strong emphasis is put on algorithms and efficient implementation.

Roughly every two weeks a new practice sheet is given to the participants. The tasks will be worked on in small groups. The time needed will be six hours a week.

Requirements

Basic experience in Python is a necessary requirement. Further, the Python packages Numpy and Matplotlib will we used. The corresponding websites provide introductions which are sufficient for our purposes. All programming tasks are done using Jupyter notebooks. For the case of having no experience in the mentioned tools we recommened to spend a little time familiarizing yourself with these before the course starts.