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Participation spots are mostly assigned on a random basis. Please keep in mind that auditing students and Nebenhörer can only participate if less than the maximum number of regular TU students register for the course (http://www.studsek.tu-berlin.de/menue/studierendenverwaltung/gast_und_nebenhoererschaft/parameter/en/). Participation spots are mostly assigned on a random basis. Please keep in mind that auditing students and Nebenhörer can only participate if less than the maximum number of regular TU students register for the course (http://www.studsek.tu-berlin.de/menue/studierendenverwaltung/gast_und_nebenhoererschaft/parameter/en/). Your participation will be confirmed soon after the application deadline.

Beginners Workshop Machine Learning

The workshop takes place during the semester break and is 2 weeks long.

Enrollment / Limited number of participants

If you intend to participate, please send an e-mail to yeom@tu-berlin.de or philipp.seegerer@tu-berlin.de with title "Beginners Workshop Enrollment" and this text:

Name: Your name
Matr.Nr: Your student ID (Matrikelnummer)
Degree: The degree you are enrolled in and want to use this course for.
TU student: Yes/No (Are you a enrolled as a regular student at TU Berlin?)
Other student: If you are not a regular student, please write your status.
ML1: Yes/No (Did you take the course Machine Learning 1 at TU Berlin?)
Other ML course: If you did not take ML1 at TU Berlin, please write if you took any equivalent course.

Participation spots are mostly assigned on a random basis. Please keep in mind that auditing students and Nebenhörer can only participate if less than the maximum number of regular TU students register for the course (http://www.studsek.tu-berlin.de/menue/studierendenverwaltung/gast_und_nebenhoererschaft/parameter/en/). Your participation will be confirmed soon after the application deadline.

Workshop topics include:

1. Math and Python recap

2. Machine learning basics

3. Clustering

4. Classical and linear methods

5. Bayesian learning

6. Support vector machines

7. Kernels

8. Neural networks

IDA Wiki: Main/SS19_BeWo (last edited 2019-06-14 16:34:09 by SergejDogadov)