Advanced AI in Businesses and Organiztions

Quick Info: Master | Seminar | English | Summer Semester | 6 ECTS

Summary

Description

In this seminar, students will implement an advanced machine learning project. The machine learning project should be of value to the decision-making in businesses, organizations, and society. We consider this an advanced course for specialization.

This is a hands-on course where students are asked to implement machine learning in a self-determined way and eventually present the results to a scientific audience (video presentation). As such, the focus is almost exclusively on methodological aspects that arise from the underlying mathematics.

During the project, students will need to engage in the following tasks related to practical implementations:

• Pre-process data to transform it into relational structures
• Apply statistical software (e.g., R and/or Python) to perform business analytics in practice
• Evaluate the results to choose the best-performing method

Outline

• Final dates and times will be shared via Moodle.
• Coaching sessions by individual arrangement.
• Submission of seminar paper and video presentation via Moodle.

Literature

• James, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer.
• In-depth introduction to machine learning in 15 hours of expert videos
• Wickham: R for Data Science.
• Kuhn & Johnson. Applied Predictive Modeling. Springer.
• Hastie, Tibshirani & Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
• Goodfellow, Bengio, Courville (2016): Deep learning. MIT Press.
• R-Bloggers features regularly worked examples (with R)