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Practical Lab SS 19 Practical Lab Numerical Simulation

Algorithms in Machine Learning and Their Application

Under direction of
Prof. Jochen Garcke
Assistant
Jannik Schürg
Date
Wednesdays, 14:15 to 16:00 in room 2.035 Endenicher Allee 19b (preliminary)
Tutorial
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Contact
Please use ed tod nnob-inu tod sni ta ballma tod b@foo tod de to contact us.

This summer semester the practical lab is only offered as a master module.

Registration

Please send an e-mail to the contact address above. If the number of registrations exceeds our limit, we will randomly choose the participants among all registrations. We will do so in three steps: First all registrations before March, 15th, are considered. Then, all registrations before April, 1st. Finally, if there are any capacities remaining, all registrations after that date are considered.

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. Depending on the technical proficiency, the time needed will be about 6 hours a week.

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.

Requirements

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