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
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Shuk-ki Lam
AI for Managers
Content
Students
Format
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- Trainer/in: Stefan Feuerriegel
- Trainer/in: Dennis Frauen
- Trainer/in: Konstantin Heß
- Trainer/in: Emil Javurek
- Trainer/in: Shuk-ki Lam
- Trainer/in: Valentyn Melnychuk
- Trainer/in: Simon Schallmoser
- Trainer/in: Maresa Schröder
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
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Shuk-ki Lam
- Trainer/in: Yuchen Ma
- Trainer/in: Valentyn Melnychuk
- Trainer/in: Simon Schallmoser
Digital Technologies, Business Analytics and Management
Content
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
- Trainer/in: Octavia-Andreea Ciora
- Trainer/in: Dennis Frauen
- Trainer/in: Dominique Geißler
- Trainer/in: Emil Javurek
- Trainer/in: Shuk-ki Lam
- Trainer/in: Abdurahman Maarouf
- Trainer/in: Valentyn Melnychuk
- Trainer/in: Simon Schallmoser
- Trainer/in: Bastian Wurm
Introduction to AI
Content
Students
Format
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- Trainer/in: Dominique Geißler
- Trainer/in: Konstantin Heß
- Trainer/in: Shuk-ki Lam
- Trainer/in: Valentyn Melnychuk
- Trainer/in: Simon Schallmoser
- Trainer/in: 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
- Trainer/in: Dominik Bär
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Kerstin Forster
- Trainer/in: Shuk-ki Lam
- Trainer/in: Simon Schallmoser
Business Analytics in Practice
Content
Students
- M.Sc. Betriebswirtschaftslehre
Report and Presentation
- Report (44,400 – 66,600 characters, including spaces)
- Presentation (30-50 minutes)
Enrolment
- Please register via Projektkursanmeldung
- Accepted students will be enrolled to the Moodle course by us
Requirements
- Previous participation in one of our courses such as Digital Technologies, Business Analytics and Management (FSG).
- Sufficient programming skills required (e.g. R, Python), such as taught in one of our previous courses (e.g. "Introduction to AI").
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Shuk-ki Lam
- Trainer/in: Katharina Riepl
- Trainer/in: Simon Schallmoser
- Trainer/in: Jonas Schweisthal
AI for Good
Content
In this seminar students will
apply AI methods on real world data to solve societal problems, by analyzing data and applying different AI methods to visualize and gain insights. Topics are selected with a focus on having a positive impact on society. Students are encouraged to work in teams of four.
Students
- MMT
- Master programs at the faculty of mathematics, informatics and statistics
Seminar Paper
Deliverables will be a seminar paper (22,200 characters per Person, including spaces) and a video-presentation (15 minutes per Person).
Enrolment
Moodle self enrolment, enrolment key via LSF
Requirements
Sufficient programming skills required (e.g. R, Python), such as taught in one of our previous courses ("AI for Managers", "Introduction to AI", etc.).
- Trainer/in: Dominik Bär
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Kerstin Forster
- Trainer/in: Shuk-ki Lam
- Trainer/in: Simon Schallmoser
Managerial AI
Content
In this seminar, students will apply AI methods on real world data to solve managerial problems (e.g. prediction of financial outcomes, internet search trends, etc.). Participants will analyze data and apply different AI methods to visualize and gain insights on their data. Students are encouraged to work in teams of two.
Students
- M.Sc. Betriebswirtschaftslehre
Seminar Paper
Deliverables will be a seminar paper (22,200 characters per Person, including spaces) and a video-presentation (15 minutes per Person).
Enrolment
Moodle self enrolment, enrolment key via LSF
Requirements
Sufficient programming skills required (e.g. R, Python), such as taught in one of our previous courses ("AI for Managers", "Introduction to AI", etc.).
- Trainer/in: Dominik Bär
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Kerstin Forster
- Trainer/in: Shuk-ki Lam
- Trainer/in: Simon Schallmoser
Methods for AI
Content
In this seminar, students will discuss a specific AI method and its application in practice. Participants will study the theoretical foundations of the method, and discuss how to apply it to data.
Students
- M.Sc. Betriebswirtschaftslehre
- MMT
Seminar Paper
Deliverables will be a seminar paper (22,200 characters, including spaces) and a video-presentation (15 minutes).
Enrolment
Moodle self enrolment, enrolment key via LSF
Requirements
Sufficient programming skills required (e.g. R, Python), such as taught in one of our previous courses ("AI for Managers", "Introduction to AI", etc.).
- Trainer/in: Dominik Bär
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Kerstin Forster
- Trainer/in: Shuk-ki Lam
- Trainer/in: Simon Schallmoser
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).
Enrolment
- Please register via Hauptseminaranmeldung
- Accepted students are asked to self enroll via Moodle, enrolment key in LSF
Requirements
Sufficient programming skills required (e.g. R, Python), such as taught in one of our previous courses (e.g. "Introduction to AI").
- Trainer/in: Dominik Bär
- Trainer/in: Stefan Feuerriegel
- Trainer/in: Kerstin Forster
- Trainer/in: Shuk-ki Lam
- Trainer/in: Simon Schallmoser