python

Lecturer: Hans Fangohr
Hands-On exercises: Hans Fangohr, Henning Glawe and Heiko Appel

The course has been designed for scientists and engineers to teach the practical programming skills that are relevant for modern computational science. The module does not assume prior programming knowledge of participants. The module uses hands-on activities for all participants to exercise and experiment with the taught material. The material covers a wide spectrum of skills that are advantageous for scientists who need to handle data - be it from experiment or simulation – and provides a basis for self learning or directed learning of more specialized topics at a later stage.

Topics include:

- Introduction to data types in Python
- Control flow
- Name spaces
- Input/Output
- Higher order functions
- Main programming paradigms
- Important Python modules for computational science (numpy, scipy, pandas, sympy)
- Data visualization with matplotlib

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is open to students, researchers, and clinicians keen to learn the essentials of machine learning and implementing it via Python. The aim of the course is to provide a comprehensive map of machine learning (and deep learning) methods with no specific background requirements. A little background in python can be helpful, though. We will focus on fundamentals of machine learning, validation methods, linear and nonlinear models, and feature reduction. The students will also get familiarized with the Python packages of Sci-kit Learn and Pytorch. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics
Types of machine learning: supervised and unsupervised
Validation  metrics and cross validation
Introduction to linear and nonlinear models include: Linear regression, Random forest, support vector machines, deep neural networks.
Feature reduction.
Regularization.