== Python Programming for Machine Learning ==

Python Programming for Machine Learning is an optional course in the module "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.

Track A is recommended for ML1 students as it has no time conflict with that course.

 || '''Course Period:'''    || 16 Oct 2017 - 10 Nov 2017 ||
 ||<(^|2> '''Lectures:'''   || Mon 08:00-10:00 in room H 2013 [Track A] ||
 ||                            Mon 16:00-18:00 in room H 2013 [Track B] ||
 ||<(^|3>  '''Exercises:''' || Tue 08:00-10:00 in rooms TEL 206re, TEL 106li [Track B] ||
 ||                            Wed 08:00-10:00 in rooms TEL 106re, TEL 106li [Track A] ||
 ||                            Fri 08:00-10:00 in rooms TEL 106*, TEL 206*, MAR 6057, MAR 6001 ||
 || '''Exam:'''    || Thu 23 Nov 2017 from 08:15 to 09:45 in room H 0105 ||
 || '''Language:'''  || English  ||
 ||<(^|2>'''Trainers''' || Grégoire Montavon ||
 ||                        Sergej Dogadov ||
 ||'''Contact''' || sergej.dogadov@campus.tu-berlin.de ||
 || '''ISIS:''' || https://isis.tu-berlin.de/course/view.php?id=11205 ||

'''Frequently Asked Questions (FAQ)'''

 * '''When does the course start?''' Monday 16 October 2017
 * '''How to register for the course?''' Pre-registration is not needed.


'''Main Topics'''

 * Python Basics
 * Numpy / Performance / Plotting
 * Rounding / Overflow / Linear Algebra
 * Randomness / Simulation

'''Additional Topics'''

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


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 ===
If you are not a student at TU and want to earn credit, you have to solicit [[http://www.tu-berlin.de/?id=76326|''Nebenhörerschaft'']]: 
 * print out the forms concerning Nebenhörerschaft you find on that page
 * pass by at my office (see above) 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).