CAS ETH in Cloud and Mobile Computing
Applications for the Fall programme will be accepted from 1 – 31 May! Apply here.
If you have questions after reviewing the website, please contact the Programme Manager for a consultation.
The CAS in Cloud and Mobile Computing (CAS CMC) provides a targeted technical education in cloud, mobile and edge computing with AI to advance the careers of industry managers.
As organisations become more digital and incorporate AI, they are also becoming increasingly dependent on networks to transform data into value. Even seemingly simple decisions such as whether to keep data storage and processing in house or in the cloud can have significant implications. Managers without any formal training in these areas are at a disadvantage when making critical resource allocation and operational decisions that can have significant impacts on corporate competitiveness. This is where the CAS CMC comes in.
The CAS CMC provides a targeted education in wireless networks, communication and cloud & edge computing with AI to business managers to advance their career. The aim of this programme is to improve the decision-making of managers by providing them with fundamental training in communication technologies and the implementation of AI-related business applications over wide area networks.
The target audience for this program is business managers who need to make technology-related decisions in their companies. A prior technical degree is not required. The program is appropriate for both managers who have non-technical degrees (business, economics, law, social science, etc.) and managers with technical backgrounds who have gaps or want to refresh their knowledge with fundamental technical training in these areas.
Graduates will be able to communicate better and develop stronger relationships with IT and AI technical teams and staff, particularly in relation to implementing AI applications that interact with external environments over networks. In turn, this will enable them to extend their existing management skills to take on more challenging leadership roles in interdisciplinary projects with significant AI applications.
Modules
Wireless Data Communication – Dr. Stefan Mangold
This course provides foundational knowledge of data networks and wireless communications, focusing on current networking standards, radio physics, and regulations. It also reviews today's standards for Wi-Fi, Bluetooth, Cellular 5G, Satellite, Visible Light, and Audio Communication Networks, and explains the general principles behind them. The course also shares industry insights from the telecommunications, toy, and medical technology sectors.
Cloud Computing – Dr. Danica Porobic
The objective of this course is to provide a systems view of cloud computing through a data management lens and its practical implications on everyday business. The course will give participants a better understanding of how infrastructure, architecture and software work together to provide a valuable service, and then will show how cloud computing is changing with the increasing use of AI. We will also explore how selected newer technologies, some driven by the needs of AI, are poised to have a significant impact on cloud operations and usage in the future.
Mobile Computing – Dr. Stefan Mangold
This course explores the fundamentals of mobile computing in environments with limited or unreliable network connectivity and constrained computing resources. Participants will gain hands-on experience with electronic devices and software simulators to understand data processing and the successes and failures of mobile A.I. decision-making in real-world scenarios. The course also features insights from various industries, including telecommunications, toy, and medical technology.
Innovating with GenAI: Use Cases, Implementation, and Governance – Dr. Sina Wulfmeyer
This course is still under development and subject to change. The planned course will explore the integration of GenAI into cloud and mobile computing, learn how to prioritize GenAI use cases, define operational prerequisites for successful deployment, and set governance strategies to ensure responsible GenAI implementation.
For more information about the CAS DML, please visit the programme webpage.
Contact
ETH Zurich
Department of Computer Science
Andreasstrasse 5
OAT Z 22.1
8092
Zurich
ETH Zurich
Rämistrasse 101
HG E 17-18.5
8902
Zurich