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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).
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 || '''Exam:''' || Wednesday 31.05.2017 from 16:15 to 17:45 in room EB 301 ||  || '''Exam:''' || Wednesday 31.05.2017 from 16:15 to 17:45 in room H 0104 ||
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 || '''ISIS:''' || TBD ||  || '''ISIS:''' || https://isis.tu-berlin.de/course/view.php?id=10158 ||
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[[http://www.python.org/|Python]] has become a standard language for prototyping and plotting results in the machine learning community. Goal of this course is a basic understanding of python programming for machine learning and data analysis. We want to enable students to quickly load a data set, implement an algorithm, run analyses and plot the results. We will therefore focus on efficient calculations and visualization. For this, we make use of the packages
 * numpy
 * matplotlib
 * scipy
Examples relate to Machine Learning Applications.		 '''Frequently Asked Questions (FAQ)'''

 * '''When does the course start?''' The course starts on 21 April 2017 in room H 0107
 * '''How to register for the course?''' Pre-registration is not needed. Only exam registration is necessary.
 * '''How many credits is the course?''' 3 ECTS if taken as a standalone course (e.g. Nebenhörerschaft, Erasmus).




'''Main Topics'''

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

'''Additional Topics'''

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

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).

Frequently Asked Questions (FAQ)

  • When does the course start? The course starts on 21 April 2017 in room H 0107

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

  • How many credits is the course? 3 ECTS if taken as a standalone course (e.g. Nebenhörerschaft, Erasmus).

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 ''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).

IDA Wiki: Main/SS17_PythonKurs (last edited 2017-05-31 16:51:20 by GrégoireMontavon)