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Preparatory Course: Mathematics and Computer Science for Modeling

The "Computer Science and Mathematics" preparatory course will combine a hands-on introduction to programming in python with a revision of elementary mathematical concepts. The topics include data types, data structures, control structures and data visualisation on the programming side and they will be applied to vector/matrix calculation, integration/differentiation of functions and differential equations.

The course is meant for students who are about to start the Cognitive Science Master Program at the Ruhr-University .



Course type
Lab courses
Winter Term 2022/2023

Teaching Units

Unit 1: Introduction to Programming in Python
Document Jupyter notebook
Link Python documentation
Reference solution Jupyter notebook with all exercise solutions
Link Bonus exercises with varying difficulties
Lecture slides Slides
Programming Session I
Lecture slides Slides
Document How to fix Spyder bug
Reference solution Solution Task 1
Reference solution Solution Task 2
Reference solution Solution Task 3
Unit 2: Functions in Math
Link Interactive simulator for function transformations
Reference solution Exercise 1 Solution
Link Article on function translation / shift
Link Article on function compression, stretching and reflection
Document Jupyter notebook
Reference solution Exercise 2 Solution
Reference solution Exercise 3 Solution
Lecture slides Lecture slides
Document Function transformation example

This example shows how function transformations (translation, compression along the x axis and stretching along the y axis) can be used to match a Gaussian distribution to data points.

Unit 5: Integration
Lecture slides Lecture slides
Unit 6: Differential Equations
Lecture slides Lecture slides


Document Exam topics

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