Course The course is aimed at managers, project managers and interested parties. The course makes it easier for participants to recognize use cases for AI and discuss them with specialists. Specifically, participants will get to know …

• the key terminology from artificial intelligence and machine learning (ML).
• the functioning and properties of artificial neural networks.
• strengths, weaknesses and risks of ML, and the resulting economic consequences.

• 07.05.2024 - 08.05.2024, 09:00 - 17:00, Online (Virtual) Language: German. Costs: CHF 1400

Early-Bird Discount: 20% up to 2 months before course start, 10% up to 1 month before course start. No VAT (MwSt) is charged. → Sign up now

The next dates will be determined based on demand. If you are interested, please contact me (→ Contact) so that I can inform you and to help me choose course locations.

Prior Knowledge No prior knowledge is required. A certain Computer science affinity and a math / statistics basic understanding (high school level) are helpful, but not necessary.

Organizational Two-day course with a maximum of 10 participants (except in virtual courses). Participants receive a certificate of participation as well as all course materials (slides) in printed and digital form.

Detailed course description As a core element, we treat the basics of modern AI and in particular the machine learning (ML) in this course: We primarily deal with the concepts and ideas on which the algorithms are based and not with mathematical details, which are of little importance for the basic understanding. This allows us to discuss and understand the basic functioning, strengths and weaknesses of modern AI and in particular of artificial neural networks within two days. From there we draw directly on economic properties of AI: Where and when can it be used? What are the biggest cost points, and why do artificial intelligences have even stronger scale economies than traditional software?

The theory is primarily conveyed via presentations and deepened via short exercises. Concrete case studies help to make the learned theory tangible. Examples of discussed case studies include:

• How does the famous AI DeepBlue, which defeated chess world champion Kasparov, work?
• Better than a human? Cancer diagnoses by an AI.
• How to customize and improve ChatGPT for your specific use-case?
• What economic advantages does Tesla’s ambitious self-driving car strategy have from a technical perspective?