The workshop will take place on July 28th, 10:00 AM CEST on Zoom
- Understand what Machine Learning is.
- Apply Machine Learning algorithms.
- Think about how you could (or already do) use Machine Learning in your own work.
The interest in Machine Learning is really spiking in governments, academia and business alike. The promise of automated decision making or self-learning data analysis algorithms is already making a difference in practice.
Our goal is to build a solid understanding of what Machine Learning is. How you can build simple Machine Learning algorithms on your own. And—most importantly—how you can use Machine Learning in your own work. When taking this course some participants will be surprised to learn that Machine Learning already was part of their work (e.g. regression models). To some extent we want to demystify machine learning and set a solid foundation which allows you to build upon (e.g. by taking the Deep Learning course the next day).
The course will alternate between lecture style input and applied workshop phases. We will pro- vide R Markdown workbooks that make it easy to implement your first Machine Learning mod- els. This class relies on R, but we expect no prior R knowledge. We will work with the easy to use RStudio Cloud computing environment. Some knowledge of basic linear algebra is helpful.
Marcel Neunhoeffer is a Ph.D. Candidate and Research Associate at the chair of Political Science, Quantitative Methods in the Social Sciences at the University of Mannheim. His research focuses on quantitative methodology, where he is specifically interested in the application of deep learning algorithms to social science problems. Substantively, he is interested in the prediction of political behavior and the ethical implications of new trends in applied social science research like Big Data and Artificial Intelligence with a focus on privacy. He is also a co-founder, contributor and the visualizationist of zweitstimme.org – a website that communicates a scientific forecast for German Federal elections to a broad audience.
Introduction to Machine Learning (Part I)
Introduction to Machine Learning (Part II)
All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.