CAS AMI: Applied Machine Learning & Information Processing (former AIT)
For managers and executives driven to explore hands-on machine learning and information processing technology. Prepares participants to be more technology savvy and drive digitalization strategies in their organizations.
The Certificate of Advanced Studies ETH in Applied Machine Learning & Information Processing (CAS AMI, former CAS in Applied Information Technology) provides managers, executives and other professionals with technical knowledge in processing information and data, as well as up to date insights in Machine Learning (ML) technologies.
The programme will provide participants with valuable foundational understanding on:
- Programming principles as a key tool
- 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 differentiating skills that enable them to:
- Recognize the impact of disruptive information processing technologies (such as AI) and adapt accordingly
- Understand the risks of technology and implementation use cases
- How to take into account complex ethical issues related to digitalization
- Extract insights from complex data and apply data-driven decision making
- Build stronger interactions and drive successful projects with their teams of digitalization experts
- Train logical thinking and problem-solving abilities
A previous technical degree is not required to attend this programme.
Participants will complete 6 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.
Prof. Dr. Ender Konukoglu (Computer Vision Lab, ETH Zurich)
This online module offers a practical introduction to some basic concepts and techniques for information processing as well as practical mathematical notions.
Participants are introduced to programming using chatbots. They learn also about introductory mathematical models required to understand the machine learning concepts. This module also serves as a preparation for modeling and programming tasks in the other modules.
Dr. Oylum Akkus
This module covers the essential concepts and tools of data science. The main purpose is to provide participants the basic knowledge and intuition to use data and understand how it is used. The participants explore the data landscape, understand key data science techniques, and learn how to apply them. The key topics of this module are the types of data, sources, and collection methods, data lifecycle, data-driven decision making, exploratory data analysis, experimental testing, regression models, and machine learning. Each topic will be enriched with collaborative discussions and hands-on exercise, enabling participants to develop a practical understanding of broad data science topics.
Prof. Dr. Ender Konukoglu (Computer Vision Lab, ETH Zurich)
This module covers the essential concepts and tools of machine learning.
• Introduction to Machine Learning
• Low-data learning models and hands-on sessions
• Neural Networks and hands-on exercises
• Self-supervised learning and attention
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. Participants will understand basic concepts of visual regonition and human-computer interaction systems.
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 page Grewe 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.
The topics covered in the module are:
1. Introduction to Reinforcement Learning (RL)
- Reinforcement Learning and the Brain
- Example of RL Neuroscience Experiments
- The Rescorla-Wagner Rule
- The Temporal Difference Rule
- The basic Idea of Q-Learning
2. Reinforcement Learning in Machines
- Basic Components of RL
- Policy and Value Function based RL
- MC, MRPs and MDPs
- The Bellmann (Expectation) Equation
Dr. Oylum Akkus
In the realm of data science, the Ethics, Leadership, and Communication module equips professionals with essential skills beyond technical expertise. It delves into data privacy, algorithmic bias, and responsible AI use. The module also emphasizes the importance of effective leadership in guiding data-driven initiatives, fostering collaboration, and navigating complex decision-making processes in the data world. Moreover, it highlights the significance of clear and persuasive communication to bridge the gap between data scientists and diverse stakeholders, translating technical insights into actionable strategies.
In this module we are dealing with real-world ethical dilemmas, leadership challenges, and communication strategies specific to the data science field. Through interactive discussions, and practical exercises, you will develop a comprehensive understanding of how to navigate ethical complexities, lead data-driven teams with integrity, and communicate technical information effectively to diverse audiences. Ultimately, this module empowers data scientists to become not only skilled practitioners but also ethical leaders and effective communicators who drive positive impact in their organizations and society.
CAS AMI 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 AMI 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 AMI 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 AMI (formerly CAS AIT) is 01.07.2025 (Application window open from 1 April - 1 July of each year).
The CAS ETH AMI is part of the MAS in Applied Technology programme, and provides strategic insights on key topics related to information processing and machine learning. The participants gain a solid foundational understanding of advanced technology concepts and the confidence in driving the implementation of 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. Benjamin Grewe (D-ITET)
Programme Manager: Dr. Iulian Nistor (D-ITET)
Programme Advisor: 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