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=== 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`.

Project Machine Learning (Projekt Maschinelles Lernen)

General Information

First meeting

Wednesday, 18 October 2017, 10:15am, Room MAR 4.065

Responsible

Prof. Dr. Klaus-Robert Müller

Contact

Jacob Kauffmann <jacobkauffmann@me.com>

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 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.

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.

IDA Wiki: Main/WS17_ProjectML (last edited 2017-10-10 12:47:10 by JacobKauffmann)