Introduction to Python
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 as well as data processing and mild machine learning. During the second week, participants will implement a project in Python.
Eddie Seabrook, M.Sc.Lecturer
|(+49) 234-32-27921 email@example.com NB 3/75|
Frederik Baucks, M.Sc.Lecturer
|(+49) 234-32-27988 firstname.lastname@example.org NB 3/35|
Moritz Lange, M.Sc.Lecturer
|(+49) 234-32-27988 email@example.com NB 3/35|
Pavlos Rath-Manakidis, M.Sc.Lecturer
|(+49)234-27988 firstname.lastname@example.org NB 3/35|
- Course type
- Lab courses
- 3 CP
- Summer Term 2023
- Lab course
every day from 10:00 to 16:00 in room ID 03/411.
First appointment is on 18.09.2023
Last appointment is on 29.09.2023
We expect fluency in one other programming language and familiarity with concepts like
- control structures
- data types
- object-oriented programming
These concepts will not be taught separately.
Furthermore, the course will be taking place in a room without PCs, meaning that students are required to use their own laptops during the course.
The official language of the course is English, and the exams will be given in English.
- Python basics: syntax, interpreter, control structures, data types, OOP
- Scientific computing: NumPy, Matplotlib
- Project: realization of a project in Python
Grading is based on the project in the second week. If crucial components of Python are not
covered in your project, we might also test your knowledge on the subject.
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, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210