|
⇤ ← Revision 1 as of 2019-09-04 11:32:37
Size: 2056
Comment:
|
Size: 2062
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 5: | Line 5: |
| ||'''First meeting'''|| Thursday, October 17 2019, 10:15am, Room TBA || | ||'''First meeting'''|| Wednesday, October 16, 2019, 10:15am, Room TBA || |
| Line 15: | Line 15: |
| Our first meeting is October 2019 at 10:15am. | Our first meeting is October 16, 2019 at 10:15am. |
Project Machine Learning (Projekt Maschinelles Lernen)
General Information
First meeting |
Wednesday, October 16, 2019, 10:15am, Room TBA |
Responsible |
|
Contact |
Jacob Kauffmann j.kauffmann@tu-berlin.de |
Language |
Englisch |
Credit |
M.Sc. Modul (Projekt), 9 LP (ECTS), PÄS |
Course Information |
|
Enrollment open |
|
First Meeting
Our first meeting is October 16, 2019 at 10:15am.
Enrollment / Limited number of participants
The course has a a limit of 30 participants. If you intend to participate, please send an e-mail to j.kauffmann@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.
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.