AI for Managers
Content
The course aims to give an introduction to AI algorithms and coding exercises. Students learn to plan, implement and evaluate AI in applied settings in order to generate value from data forsociety, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customersatisfaction, competitive advantage – by leveraging the growing amounts of structured and unstructured data. Please note that sufficient programming skills are required (e.g. R, Python).
Students
MMT
MBR
Format
Onsite and online course (either or, tbd)
Literature
- James, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer
- Sharda, Delen & Turban (2014): Business Intelligence: A Managerial Perspective on Analytics. Pearson
Enrolment
Moodle self enrolment, enrolment key via LSF- Enseignant: Stefan Feuerriegel
- Enseignant: Dennis Frauen
- Enseignant: Shuk-ki Lam
- Enseignant: Valentyn Melnychuk
- Enseignant: Katharina Riepl
- Enseignant: Simon Schallmoser
Advanced AI in Businesses and Organizations
Content
In this online course, 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. This is an advanced course for specialization.
Students
- MMT
- MBR
- M.Sc.
Format
Online course
Seminar Paper
Deliverables will be a paper that involves actual machine learning results (with codes in the appendix) and a video presentation. Students will need to advance their knowledge on their own with the provided materials.
Literature
- James, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer. https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
- Wickham: R for Data Science. O’Reilly. https://r4ds.had.co.nz/
- 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
- Blog: https://www.r-bloggers.com/ features regularly worked examples (with R)
Enrolment
Moodle self enrolment, enrolment key via LSF
- Enseignant: Stefan Feuerriegel
- Enseignant: Shuk-ki Lam
- Enseignant: Yuchen Ma
- Enseignant: Valentyn Melnychuk
- Enseignant: Katharina Riepl
- Enseignant: Simon Schallmoser
Digital Technologies, Business Analytics and Management
Content
Overall, the course provides students with a comprehensive overview of how digital technologies are used in modern business settings. It equips them with the skills they need to take advantage of these technologies and become more successful managers.
Students
- M.Sc. Betriebswirtschaftslehre
Requirements
No specific requirements
Literature
- James, Witten, Hastie & Ribshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer.
- Sharda, Delen & Turban. 2014. Business Intelligence: A Managerial Perspective on Analytics. Pearson.
Enrolment
Self-enrolment in Moodle, enrolment key via LSF
- Enseignant: Waltraud Broch
- Enseignant: Stefan Feuerriegel
- Enseignant: Dennis Frauen
- Enseignant: Dominique Geißler
- Enseignant: Shuk-ki Lam
- Enseignant: Valentyn Melnychuk
- Enseignant: Katharina Riepl
- Enseignant: Simon Schallmoser
- Enseignant: Bastian Wurm
Introduction to AI
Content
The course aims to give an introduction to AI algorithms and coding exercises. Students learn to plan, implement and evaluate AI in applied settings in order to generate value from data forsociety, corporations and individuals. This serves the pressing need of firms to improve their efficiency – such as customersatisfaction, competitive advantage – by leveraging the growing amounts of structured and unstructured data. Please note that sufficient programming skills are required (e.g. R, Python).
Students
B.Sc.
Format
Onsite and online course (either or, tbd)
Literature
- James, Witten, Hastie & Tibshirani (2013): An Introduction to Statistical Learning: With Applications in R. Springer
- Sharda, Delen & Turban (2014): Business Intelligence: A Managerial Perspective on Analytics. Pearson
Enrolment
Moodle self enrolment, enrolment key via LSF- Enseignant: Stefan Feuerriegel
- Enseignant: Dennis Frauen
- Enseignant: Dominique Geißler
- Enseignant: Konstantin Heß
- Enseignant: Shuk-ki Lam
- Enseignant: Valentyn Melnychuk
- Enseignant: Katharina Riepl
- Enseignant: Simon Schallmoser
- Enseignant: Maresa Schröder
AI Tools for Management and Social Science
Content
In this seminar, students will familiarize with a specific AI tool relevant for management and social science research. They will review a specific topic, write a report and give a presentation on their findings.
Please note: The successfull completion of this course is a prerquisite to write your bachelor thesis at the Institute of AI in Management.
Students
- B.Sc. Betriebswirtschaftslehre
Seminar Paper
Deliverables will be a seminar paper (22,200 characters, including spaces) and a video-presentation (15 minutes).
Requirements
Sufficient programming skills required (e.g. R, Python), such as taught in one of our previous courses (e.g. "Introduction to AI").
Enrolment
- Please register via Hauptseminaranmeldung
- Accepted students are asked to self enroll via Moodle, enrolment key in LSF
- Enseignant: Dominik Bär
- Enseignant: Stefan Feuerriegel
- Enseignant: Shuk-ki Lam
- Enseignant: Katharina Riepl
- Enseignant: Simon Schallmoser