Machine Learning – Advanced Data Analysis Concepts with Python

About

A challenging task depicts the analysis of complex data for unravelling deeper insights from and predicting future patterns based on available data. Machine learning (ML) algorithms provide powerful approaches to extend the data analytics spectrum beyond classical statistical modelling concepts. The workshop aims to enable the participants to understand and apply supervised and unsupervised learning methods and to address advanced data analysis tasks in a sustainable pythonic manner.
Based on Python, the workshop presents state-of-the-art ML libraries. To fully exploit the potential of the provided ML approaches, beneficial implementation strategies into the workflow of data analysis projects are elaborated.

The workshop introduces the guiding principles of data science and ML approaches used for advanced data analysis via hands-on examples covering:
- introduction to supervised and unsupervised machine learning
- discussion of statistical principles guiding machine learning concepts
- discussion of interpretability approaches to machine learning algorithms
- application of open source libraries for advanced data analysis and machine learning
- usage of an agile working environment via GitLab projects

Course leader

Dr. rer. nat. Nadine Berner, Forschungs- und Innovationszentrum (FIZ) der BMW Group, München, Germany

Target group

female STEM-students and professionals

Fee info