== Bayesian Analysis with Python ==

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

It's '''not''' possible to take the Bayesian Analysis with Python course as a standalone course.

Participation in the exercise sessions is '''not''' mandatory but highly recommended. However, homework assignments have to be submitted every week.


 || '''Course Period:'''    || from January 7th, 2020 to February 11th, 2020 ||
 ||'''Lectures:'''   || Tuesday 14:15 - 15:45 in A 053 ||
 ||<(^|2> '''Exercises'''   || Friday    10:15 - 11:45 in TEL 106li    ||
 ||                            Friday    12:15 - 13:45 in TEL 106li    ||
 || '''Test:'''    || TBA ||
 || '''Language:'''  || English  ||
 ||<(^|2>'''Trainers''' || Sergej Dogadov ||
 ||                        Dr Shinichi Nakajima ||
 ||'''Contact''' s.dogadov@tu-berlin.de ||
 || '''ISIS:'''  https://isis.tu-berlin.de/course/view.php?id=18422 ||

'''Frequently Asked Questions (FAQ)'''
 * '''When does the course start?''' on Tuesday January 7th, 2020 at 14:15 
 * '''How to register for the course?''' Pre-registration is not needed. However, register for the ISIS course in time to be able to submit the exercises.


Knowledge of elementary programming concepts with Python is required. Be aware that lack of such knowledge will '''significantly''' increase the time demand for 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 [[http://www.tu-berlin.de/?id=76326|''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).