Digital Transformation & Artificial Intelligence 2: Markets & Companies
Niveau
First study cycle, Bachelor
Learning outcomes of the courses/module
The participants:
• can explain and critically reflect on the fundamental concepts, methods and fields of application of AI.
• can assess the opportunities and risks of AI for companies and society.
• can apply and adapt traditional business administration models and theories to AI-related questions.
• can classify and evaluate AI tools and frameworks in the context of digitalization and business practice.
• analyze specific AI applications and projects in companies and develop proposed solutions.
• are able to work on real-world problems using AI methods, both independently and in a team, and to communicate the results in an audience-appropriate way.
• can explain and critically reflect on the fundamental concepts, methods and fields of application of AI.
• can assess the opportunities and risks of AI for companies and society.
• can apply and adapt traditional business administration models and theories to AI-related questions.
• can classify and evaluate AI tools and frameworks in the context of digitalization and business practice.
• analyze specific AI applications and projects in companies and develop proposed solutions.
• are able to work on real-world problems using AI methods, both independently and in a team, and to communicate the results in an audience-appropriate way.
Prerequisites for the course
Successful completion of the course: Digital Transformation & Artificial Intelligence I: Basics & Tools
Course content
• Basic AI concepts, definitions and historical development
• Core concepts and methods of AI
• Fields of application of AI in a business context
• Ethics, data protection and societal impacts
• AI and traditional business administration concepts
• Practical project work and transfer
• Core concepts and methods of AI
• Fields of application of AI in a business context
• Ethics, data protection and societal impacts
• AI and traditional business administration concepts
• Practical project work and transfer
Recommended specialist literature
• Russell, Stuart J.; Norvig, Peter: Artificial Intelligence – A Modern Approach; 4th Edition, Pearson, 2021
• Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron: Deep Learning; MIT Press, 2016 (Open Access als PDF)
• Kelleher, John D.: Artificial Intelligence; MIT Press Essential Knowledge Series, 2019
• Brock, Jonathan K. L.; Wangenheim, Florian; Meier, Andreas: AI in Practice: How 50 Successful Companies Used AI and Machine; Wiley, 2019
• Buxmann, Peter; Schmidt, Holger: KI – mit Algorithmen zum wirtschaftlichen Erfolg; Springer Gabler, 2023
• Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron: Deep Learning; MIT Press, 2016 (Open Access als PDF)
• Kelleher, John D.: Artificial Intelligence; MIT Press Essential Knowledge Series, 2019
• Brock, Jonathan K. L.; Wangenheim, Florian; Meier, Andreas: AI in Practice: How 50 Successful Companies Used AI and Machine; Wiley, 2019
• Buxmann, Peter; Schmidt, Holger: KI – mit Algorithmen zum wirtschaftlichen Erfolg; Springer Gabler, 2023
Assessment methods and criteria
Portfolio assessment
Language
English
Number of ECTS credits awarded
3
Semester hours per week
Planned teaching and learning method
Blended learning
Semester/trimester in which the course/module is offered
2