CAS Cloud & Mobile Computing
Providing 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 Certificate of Advanced Studies in Cloud and Mobile Computing (CAS CMC) comes in.
The CAS CMC provides a targeted education in wireless networks, communication and cloud & edge computing with AI to managers without prior formal training in information & communication technologies (ICT) and computer science in order to advance their career. The aim of this programme is to improve the decision-making of managers by providing them with fundamental training in these fields that is applicable across multiple industries and areas of the organisation.
Please note that this CAS is not available yet and will only be offered for the first time in September 2025.
Course Descriptions
Please note that these descriptions are tentative and subject to change. Since the CAS programme is still under development, the descriptions are only meant to be indicative of our intentions as of May 2024 and do not constitute a commitment to specific content. We expect to update and finalize these descriptions in Fall 2024, but no later than January 2025.
The objective of the course is to learn the fundamentals of networking and wireless communications, including physics, frequency spectrum regulation, and standards. The most up-to-date standards used for Wi-Fi, Bluetooth, Cellular 5G Satellite, Visible Light and Audio Communication Networks are reviewed and the general principles explained. Insights from various industries are shared, including the telecommunications, toy and medical technology industries.
- Introduction and Wi-Fi
- Wireless Basics (Signal, Noise, Antenna, Modulation / Coding, Fourier, Regulation)
- Networking Standards and Architectures
- Visible Light and Audio Communication, Delay Tolerant Networks
- Internet of Things (Consumer Electronics, Interactive Toys)
- Practical experiments with Python, embedded systems, and Jemula802
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.
Potential topics (subject to change) include:
- Physics/infrastructure of the cloud: compute, storage, networking and power.
- Geography of the cloud: locality of cloud regions, high availability, connectivity, caching, and data sovereignty.
- Economics of the cloud: utility for computing power or just someone else's computers; capital or operating expense.
- Scalability of the cloud: elasticity of resources and automation of all management operations.
- Practical case studies: netflix, gmail, facebook, global bank application.
- Hands-on data processing: excel, local database, provisioned and serverless cloud database.
The objective of the course is to learn about the general principles of mobile computing and computing in edge devices where network connectivity is not assured and computing resources are constrained. Participants will work with electronic devices and software simulators to better understand how data processing and A.I. assisted decision-making works and also fails under real world conditions. Insights from various industries are shared, including the telecommunications, toy and medical technology industries.
- Signal Processing and Time Series, Feature Detection, Correlation, Machine Learning
- Dynamic Spectrum Assignment with Reasoning
- Spectrum Regulation, Open Access, Multi-Stage Games
- Fine Tuning and Using an A.I. Large Language Model (LLM) for Decision-Making
- Mobile and Edge Computing
- Practical experiments with Python, Jemula802, Local LLM
Additional Information
Participants complete 4 modules over 4 months. Courses are generally conducted in either a block format or blended learning format to minimize time away from work. Classes are held at ETH Zentrum campus on seven weekends, each consisting of one full day (Friday) and one half-day (Saturday morning). Thus, this CAS is well suited as a part-time study programme.
Workload is approximately 300 hours.
Study language is 100% English.
CAS CMC applicants must satisfy the following requirements:
- ETH recognised university degree at Master level or equivalent educational background
A bachelor degree can be exceptionally considered sur dossier.
- Demonstrated managerial experience
At least 5 years of professional work experience that includes some experience with allocation of corporate resources, e.g. line management, project management, etc.
- Good knowledge of English
At least B2 level is recommended.
MAS AID participants and applicants have priority over CAS only applicants. Prior completion of the CAS in Data & Machine Learning (CAS DML) and CAS in AI and Software Development (CAS AIS) is recommended, but not required. In particular, programming experience equivalent to completing both the CAS DML and CAS AIS is highly recommended. Preference in admissions is given to applicants that have completed the CAS DML and CAS AIS or have some basic knowledge of programming and computer science that would make following this CAS more beneficial for the participant.
Applications will be reviewed by the Admission Committee. The final decision is communicated by the School for Continuing Education.
Please note that this CAS is not offered until September 2025 and applications will not be accepted before Spring 2025.
When appropriate, please apply online through the School for Continuing Education website. After submitting the application and uploading supporting documentation, you will be asked to pay the non-refundable application fee.
Deadline: 31 May 2025
The CAS CMC tuition fees and ECTS can be fully credited towards the MAS in AI and Digital Technology. Successful graduates of the CAS CMC who wish to continue can apply to the MAS in the same manner as any other applicant. Prior completion of CAS will be considered during the application review, but there is no automatic right of admission to the MAS after completing individual CAS programmes.
Questions
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