|MODULBEAUFTRAGTE:R:||Prof. Dr. Laurenz Wiskott|
|DOZENT:IN:||Eddie Seabrook, M.Sc.|
|FAKULTÄT:||Fakultät für Informatik|
|ANGEBOTEN IM:||jedes Sommersemester|
Two week lab-course (Blockkurs in der vorlesungsfreien Zeit)
LERNZIELEAfter the successful completion of this course the students ∙ will know and be able to apply basic syntax and structure of Python 3, ∙ will understand numerical representations and processing of data using Numpy, ∙ will have gained first practical experience in planning and conducting a small project in a team using Python 3.
INHALTPython is a programming language that is wide-spread among scientists due to its readability and powerful standard libraries. This practical course teaches Python 3 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 possibly simple machine learning. All course-work is done in teams of two. During the first week, participants will work on Jupyter notebooks autonomously and discover Python 3 in a largely self-taught manner. Teaching assistants are present and support is provided if required. During the second week, participants will implement a project in Python 3 using the previously acquired skills. We provide a default project, usually from the area of machine learning. Alternatively, own project ideas can be realized if discussed early on with the Teaching Assistants.
VORAUSSETZUNGEN CREDITSBestandene Modulabschlussprüfung
EMPFOHLENE VORKENNTNISSEWe expect fluency in one other programming language and familiarity with concepts like - loops and control structures (while, for, if . . . ), - basic data types and structures (boolean, int, float, string, arrays, ...), - functions, and - object-oriented programming. These concepts will not be taught separately. A solid understanding of basic maths and algorithms is also recommended for a successful project.