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Track A is recommended for ML2 ("Maschinelles Lernen - Theorie und Anwendung") students as it has no time conflict with that course. Participation in the exercise sessions is not mandatory but highly recommended.
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 || '''Course Period:''' || from 16 April 2018 to 11 Mai 2018 ||
 ||<(^|2> '''Lectures:''' || [Track A] Monday 10:15 - 12:00 in EB 301||
 || [Track B] Monday 14:15 - 16:00 in A 053||
 ||<(^|6> '''Exercises (optional):''' || Tuesday 08:15 - 10:00 in TEL 106li & 206re||
 || Tuesday 14:15 - 16:00 in TEL 106li ||
 || Tuesday 16:15 - 18:00 in TEL 106li ||
 || Wednesday 08:15 - 10:00 in TEL 106li & 106re ||
 || Friday 08:15 - 10:00 in TEL 106li/re & 206li||
 || Friday 08:15 - 10:00 in MAR 6.001 & 6.057 ||
 || '''Exam:''' || TBA ||
 || '''Course Period:''' || from Oct 15, 2018 to Nov 9, 2018 ||
 ||<(^|2> '''Lectures:''' || [Track A] Monday 10:15 - 11:45 in EMH 225||
 || [Track B] Monday 16:15 - 17:45 in H 2032||
 || '''Exam:''' || Nov 15, 2018, 14:00 (presumably) ||
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 ||<(^|2>'''Trainers''' || Sergej Dogadov ||  ||<(^|3>'''Trainers''' || Sergej Dogadov ||
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 ||'''Contact''' || s.dogadov@tu-berlin.de ||
 || '''ISIS:''' || TBA ||
 || David Lassner ||
 ||<(^|2> '''Contact''' || philipp.seegerer@tu-berlin.de ||
 || lassner@tu-berlin.de ||
 || '''ISIS:''' || https://isis.tu-berlin.de/course/view.php?id=13921 ||
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* '''When does the course start?''' on Monday 16th April 2018 at 10:15 [Track A] or 14:15 [Track B]
 * '''When does the course start?''' on Monday Oct 15th, 2018 at 16:15 [Track A] or 14:15 [Track B]
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'''Main Topics''' Knowledge of elementary programming concepts will be helpful. Be aware that lack of such knowledge will increase the time demand of the class.
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 * Python Basics
 * Numpy / Performance / Plotting
 * Rounding / Overflow / Linear Algebra
 * Randomness / Simulation
Homework is submitted via ISIS (see above), therefore you have to register there.
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'''Additional Topics'''

 * Linear Models (Regression, PCA)
 * Low-Level Extensions (F2PY, cython)


Knowledge of elementary programming concepts will be helpful. Be aware that lack of such knowledge will increase the time demand of the class. In that case, you should consider to prepare with a python beginner class.

Homework is submitted via the ISIS page.

=== students from other universities ===
=== Students from other universities ===
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 * pass by at my office (MAR 4.034) to have them signed  * pass by at Mr. Dogadov's office (MAR 4.034) to have them signed

Python Programming for Machine Learning

Python Programming for Machine Learning is an optional course in the modules "Machine Learning 1", "Cognitive Algorithms" and "Machine Learning - Theory and Applications" and is worth 3 LP (3 ECTS credits).

In the general case, it is not possible to take the Python course as a standalone course. There are possible exceptions to this (e.g. it complements another ML or related course you are taking in parallel). In that case, a special request needs to be made.

The course will be split in two tracks A and B, with the same content, but held at different times of the week/day. Students can opt for either of these.

Participation in the exercise sessions is not mandatory but highly recommended.

Frequently Asked Questions (FAQ)

  • When does the course start? on Monday Oct 15th, 2018 at 16:15 [Track A] or 14:15 [Track B]

  • How to register for the course? Pre-registration is not needed.

Knowledge of elementary programming concepts will be helpful. Be aware that lack of such knowledge will increase the time demand of the class.

Homework is submitted via ISIS (see above), therefore you have to register there.

Students from other universities

If you are not a student at TU and want to earn credit, you have to solicit ''Nebenhörerschaft'':

  • print out the forms concerning Nebenhörerschaft you find on that page
  • pass by at Mr. Dogadov's office (MAR 4.034) to have them signed
  • in addition, the dean of faculty IV has to sign
  • register at the the Campus Center. You will receive a TUBIT account (see below).

IDA Wiki: Main/PythonKurs (last edited 2018-10-19 10:22:42 by PhilippSeegerer)