Scientific Computing with Python

Course is already full.
Python is a programming language that is wide-spread among scientists due to its readability and powerful standard libraries. This practical course teaches Python to students with prior experience in other programming languages. In addition to introducing the language itself, we will focus on scientific computing including vectors and matrices. During the second week, participants will implement their own project in Python.

Lecturers

Details

Course type
Lab courses
Credits
3 CP
Term
Summer Term 2015

Dates

Lab course
Takes place every day from 10:00 to 18:00 in room NA 04/494.
First appointment is on 21.09.2015
Last appointment is on 02.10.2015

Requirements

"Grundlagen der Programmierung" / Fluency in at least one other programming language required / Recommended preparation: "Grundlagen der Informatik" and "Datenstrukturen"

Content

  • Python basics: syntax, interpreter, control structures, data types, OOP, etc.
  • Scientific computing: NumPy, SciPy, Matplotlib, parallelization
  • Code quality: PyDoc, exceptions, debugging, profiling, unittests, git
  • Project: realization of an own project in Python

Grading

Grading is based on the realization of an own project during the course.

Registration

Please register through sending us a mail to python@ini.rub.de. Also remember to register via FlexNow or the examination office if neccessary. Students of PO13 will be given priority because participation in one of the programming courses is mandatory for them. Other students may send us a mail as well to get to the waiting list until the end of PO13 registration phase.

In your mail please include:

  • name
  • student ID number (Matrikelnummer)
  • study program (Studiengang)
  • current semester
  • Bachelor/Master
  • PO13/PO09

Documents

Lecture slides Course Material PCA

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

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