Predictive Data Analytics - An Introduction to Machine Learning

  • Type: Block course
  • Semester: Summer semester 2024
  • Place:


  • Time:

    April 8-10, 2024

  • Lecturer:

    Dr. Sebastian Lerch


Modern methods from artificial intelligence and machine learning, in particular deep learning methods based on multi-layered artificial neural networks, provide unprecedented tools for data analysis and prediction. Over the past years, they have transformed many scientific fields and have become ubiquitous in real-world applications from speech recognition to self-driving cars.

This seminar will provide a broad introduction to machine learning from statistical foundations to applications in the sciences, economics and engineering. The focus will be on modern machine learning methods for predictive data analytics such as random forests, gradient boosting machines and neural networks, their trans-disciplinary application to supervised learning tasks, and approaches to gain insight into the 'black box' of machine learning models. Lectures on the theoretical background will be accompanied by hands-on programming exercises in Python that will cover practical aspects of implementing machine learning methods for analyzing scientific and real-world datasets.

A 3-day block course of lectures and hands-on programming exercises will take place on April 8-10, 2024. Some familiarity with basic concepts of probability theory and statistics is expected, as well as basic programming skills in Python. For the programming exercises, participants are expected to bring their own laptop with Python and relevant libraries installed.

Registration: To register for the block course, please send an email to Dr. Sebastian Lerch.