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Lab "Machine Learning"

 

Basic Information
Lecturers: Gerhard Schmidt and members of the DSS chair
Room: -
Language: English
Target group: Students in electrical engineering and computer engineering
Prerequisites: Skills in Python
Registration procedure:

If you want to sign up for this laboratory, you need to register with the following information in the registration form

  • surname, first name
  • e-mail address
  • matriculation number
  • desired topic

Please note, that the registration period starts the xx.xx.20xx at 00:00 am and ends the xx.xx.20xx at 11:59 pm. All applications before and after this registration period will not be taken into account.

Registration will be possible within the before mentioned time by sending a mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it..

The registration is binding. A deregistration is possible by sending a mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it. until Friday, xx.xx.20xx at 11:59 pm. All later cancellations of registration will be considered as having failed the lab.

Requirements, rules and commitments depend on the chosen topic and will be announced at the preliminary meeting (attendance is mandatory).

Attendance at all of the final presentations is mandatory as well to pass the lab.

The lab "Machine Learning" and the preliminary meeting will take place with an online support.

Time:

Preliminary meeting: online meeting, xx.xx.20xx at 13:00

Final presentations: online meeting, xx.xx.20xx at 13:00

Contents: See the detailed introduction of the topics below.

 

Contents

Within this lab you will use Phython, TensorFlow and other (Phython-based) tools to learn about pattern recogntion and machine learing. To achieve this we will use data bases that are freely available to perform classification and regression approaches. These approches will vary in terms of computational complexity - from simple decision trees to complex deep neural networks (plus several "stages" in between). Furthermore, we will look at different evalution strategies in order to assess the quality in terms of the processing structure being able to gereralize to "unseen" data (not beeing part of the training data).

The lab can be done without listening to the lecture Pattern Recognistion and Machine Learning. However, it is recommended also to particpate there. In the lab we will focus on how to use the tools, without going deeply in the underlying mathematical structures. This is the objective of the before mentioned lecture.

 

Prerequisites

We assume that you have access to a computer (preferably your own notbook) that is powerful enough to let Python, TensorFlow, etc. run and that you can configure according to your needs (administrator rights). Furthermore, you should have basic knowledge in Python.

 

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