CAS AIT: Applied Information Technology
Turns information technologies into competitive advantages for your organization
The Certificate of Advanced Studies ETH in Applied Information Technology (CAS ETH AIT) prepares experienced professionals with a vision that turns information technologies into competitive advantages for their organizations.
The programme will provide participants with valuable foundational understanding on:
- Foundations of Programming principles as a key tool to handle today's complex AI programming tasks, but also business operations, automating tasks and streamlining workflows
- Data Science: From Analytics to Learning
- Computer Vision/Machine Learning: Analyze patterns, trends and correlations
- Ethics, Leaderhips & Communication in Data-Science
Participants will further gain strategic skills that enable them to:
- Recognize the impact of disruptive IT technologies (such as AI) and adapt accordingly
- Understand the risks of AI technology and how to implement it in various use cases
- Shape the IT strategy and portfolio in their organizations while taking into account complex ethical issues
- Extract insights from complex data and apply data-driven decision making
- Build stronger interactions and drive successful projects with their teams of IT experts
- Train logical thinking and problem-solving abilities
A previous technical degree is not required to attend this programme.
Participants will complete 5 modules over 14 weeks from September to November. Classes 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 every other week for one full day and one half day (typically Friday all day and Saturday morning), and the programme is thus well suited as a part-time study programme.
Total workload is approximately 300 hours and successful graduates earn a total of 12 ECTS credits.
Study language is 100% English.
Dr. L. Faessler
This online module offers a practical introduction to some basic concepts and techniques for information processing as well as practical applications.
Participants are introduced to programming with Python. They learn to develop mathematical models for real-world tasks and solve them as small projects in Python. Fundamental concepts of programming being covered include variables, types, control structures, logic, arrays, functions, and matrices. This module also serves as a preparation for modeling and programming tasks in the other modules.
Prof. Dr. Ender Konukoglu (Computer Vision Lab, ETH Zurich)
Dr. Oylum Akkus
In this module, basic paradigms and techniques in working with data will be discussed, especially towards data security, managing data decentrally, and learning from data. Participants will understand some of the concepts in detail and see the mathematics behind them.
The module in particular covers cryptography and digital signatures, networking and distributed algorithms, distributed ledger technology, as well as machine learning (supervised and unsupervised learning). For each topic, there will be a hands-on and in-depth introduction that allows participants to gain a technical understanding of key ideas. This is supported by simple and concrete examples as well as programming assignments.
Prof. Dr. Ender Konukoglu
This module will cover basic theoretical knowledge on visual recognition systems of the last two decades, mostly focusing on the most recent advancements in deep learning and convolutional neural networks.
The content starts with an introduction to neural networks and then focuses on how they are used for computer vision tasks. The theoretical knowledge will be supported with a practical session that will allow participants to gain hands-on experience with most commonly used tools and deepen their understanding of the key concepts with examples.
Prof. Dr. Benjamin Grewe (external pageGrewe Lab, ETH Zurich)
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, where the agent is provided with labeled examples, reinforcement learning relies on trial and error. The agent takes actions in the environment, receives feedback (rewards or penalties), and adjusts its behavior to maximize cumulative rewards.
Dr. Oylum Akkus
In the realm of data science, these three pillars play a critical role. Data-driven decisions impact individuals, organizations, and society, thus ethical considerations are essential to maintain trust. Effective data science leadership involves more than technical expertise and must prioritize diversity, equity, and inclusion to create a thriving data culture.
Finally, clear communication bridges the gap between data experts and non-technical stakeholders. Insights, risks, and opportunities related to data science must be conveyed in a compelling manner.
CAS AIT applicants* must satisfy the following requirements:
- Demonstrated managerial experience working with technology companies or industries (people leadership and/or project leaders)
- Good knowledge of English
- ETH recognized Master’s degree (or admission "sur dossier" for Bachelor degree)
CAS AIT applications will be reviewed by the Admission Committee of the Certificate Programme. The final decision is communicated in writing.
Important Note:
MAS AT applicants do not need to apply to the CAS AIT separately. The background of MAS AT applicants is evaluated during the MAS application review process and there are no further requirements outside of that process.
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 application fee. See the Application section of our website for more information on How To Apply as well as Selection & Admission.
The deadline for applying to the CAS AIT is 31 May (Application window open from 1 - 31 May).
The CAS ETH AIT is part of the MAS in Applied Technology programme, and provides strategic insights on key topics related to information technology. The participants gain a solid foundational understanding of advanced information technologies and the confidence in driving such technologies in their work organizations.
Structure of the MAS ETH AT Programme
Programme Director: Prof. Dr. Ender Konukoglu (D-ITET)
Programme Co-Director: Prof. Dr. Ulrike Grossner (D-ITET)
Programme Manager: Dr. Iulian Nistor (D-ITET)
Programme Advisor & Participant Experience: Karin Sonderegger Zaky (D-ITET)
For further information, please contact us - we will be more than happy to guide you in all your questions!
Email:
Phone: +41 44 632 2777