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| ||'''Contact'''|| Jacob Kauffmann <[[mailto:jacobkauffmann@me.com|jacobkauffmann@me.com]]> || | ||'''Contact'''|| Jacob Kauffmann jacob.r.kauffmann@campus.tu-berlin.de || |
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| The course has a a limit of 10 participants. If you intend to participate, please send an e-mail to jacobkauffmann@me.com as early as possible since we assign spots mostly on a first come / first serve basis. It is mandatory to attend the first meeting or e-mail the course organizer if this should not be possible. Otherwise we assume that you have no interest in the course anymore and give your spot to someone else. | The course has a a limit of 10 participants. If you intend to participate, please send an e-mail to jacob.r.kauffmann@campus.tu-berlin.de as early as possible since we assign spots mostly on a first come / first serve basis. It is mandatory to attend the first meeting or e-mail the course organizer if this should not be possible. Otherwise we assume that you have no interest in the course anymore and give your spot to someone else. |
Project Machine Learning (Projekt Maschinelles Lernen)
General Information
First meeting |
Wednesday, 18 October 2017, 10:15am, Room MAR 4.065 |
Responsible |
|
Contact |
Jacob Kauffmann jacob.r.kauffmann@campus.tu-berlin.de |
Language |
Englisch |
Credit |
M.Sc. Modul (Projekt), 9 LP (ECTS), PÄS |
First Meeting
Our first meeting is Wed 18 Oct 2017 at 10:15am in Room MAR 4.065.
Enrollment / Limited number of participants
The course has a a limit of 10 participants. If you intend to participate, please send an e-mail to jacob.r.kauffmann@campus.tu-berlin.de as early as possible since we assign spots mostly on a first come / first serve basis. It is mandatory to attend the first meeting or e-mail the course organizer if this should not be possible. Otherwise we assume that you have no interest in the course anymore and give your spot to someone else.
Description
A PDF overview is available. The course has an entry in Vorlesungsverzeichnis.
Prerequisites
There are no formal entry requirements. However, we strongly recommend
the lecture Machine Learning 1,
the course Python Programming for Machine Learning and
ideally, the Lab Course Machine Learning.
Students who do not have attended these lecture, should make sure they have the following skills:
Basic theory: Students should be fluent in probability theory, linear algebra and understand how and why mainstream learning algorithms work.
Some practical ML experience: Prior exposure to the practical application of ML algorithms is essential; students should know how to select hyperparameters and assess the performance of a trained predictor.
Python programming: All code that is to be handed in must be written in Python; students should be able to program in Python using the packages numpy and scipy.