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Receive a comprehensive overview of the CAS in Generative AI, including learning outcomes, faculty details, and enrollment steps.

At a glance

Start 13 April, 2026
Duration ca. 3 months, 10 teaching days
ECTS 10 ECTS
Cost CHF 9'800 incl. lecture notes
Language English
Method In-person lecture (Zurich)
Venue

Center for Continuing Education of the University of Zurich, Schaffhauserstrasse 228, 8057 Zurich (https://www.zwb.uzh.ch/en.html)

CAS in Generative AI: Testimonials

Generative artificial intelligence (AI) is currently a growing field of research in which the University of Zurich is excellently represented in research and teaching and in which there is a high demand for further education. The Department of Informatics and the Department of Computational Linguistics  offer a Certificate of Advanced Studies (CAS). 

In this CAS, specialists with knowledge of computer science are to enable practitioners to recognize the potentials and limitations of generative AI in 10 course days (as well as a final thesis) and to use this knowledge for their work. The CAS is open to people with a University degree who have knowledge of computer science.

Generative AI has made big waves due to its disruptive potential and now affects a wide range of disciplines. Key technologies for generative AI come from computer science and computational linguistics, and there is a great need for practical knowledge about the training, application and technical limitations of generative AI systems. At the same time, their success also raises ethical and legal questions, for example regarding the use of potentially proprietary training data.

CAS Generative AI- Modules

Target audience

The CAS in Generative AI enables experts from a broad and diverse range of backgrounds to understand how generative AI can be used in their day-to-day work. Over 10 course days, participants will have the chance to learn the many potential applications of generative AI and its limitations.

The CAS is open to people with a university degree and an interest in computer science.

  • Computer scientists
  • Managers
  • Lawyers
  • Participants with a keen interest in newest technologies

Basic programming skills are required . If you do not have prior knowledge we will provide an introductory course on Python to know the basics. No prior knowledge on Machine learning is required.

Course content

  • Generative Language Models 
  • Generative AI Beyond Language
  • Management, Ethics, and Applications

Lecturers

Our lecturers are leading experts from research and industry with an excellent track record in research and teaching. Guest speakers from the industry also offer an insights into current challenges in the field of technology and management.

For a full list of our lecturers, please visit the dedicated lecturers page.

Structure, assessment, and degree

Structure

The program is divided into three modules:

  • Generative Language Models (3 days) 
  • Generative AI beyond Language Models (3 days)
  • Management, Ethics, and Applications (4 days) 

A total of 10 ECTS are required in order to successfully complete the CAS.

Each module is worth 3 or 4 ECTS credits. An ECTS credit corresponds to a workload of around 30 hours. This comprises attendance time during the course days as well as preparatory and follow-up work.

Assessment

An assessment must be completed for each module.

A pass/fail grading scheme applies to all assessments.

Degree

A total of 10 ECTS are required in order to successfully complete the CAS and obtain a Certificate of Advanced Studies UZH in Generative AI.

Admission

University degree at bacherlor’s level or an equivalent qualification as well as basic knowledge in the fields of computer science. 

Basic programming skills are required . If you do not have prior knowledge we will provide an introductory course on Python to know the basics. No prior knowledge on Machine learning is required.

Application Deadline:

For April 2026 Edition: 30 March 2026

For October 2026 Edition: 15 September 2026

Ap

Costs

CHF 9'800.- including course documentation




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