Objectives & Overview
Your Journey into Machine Learning & AI - Build Competence Beyond Buzzwords.
Many executive programmes focus broadly on Artificial Intelligence (AI), often staying at a high level. The CAS ETH in Machine Learning in Finance and Insurance offers something different — a deep dive into the core of AI: machine learning.
Machine learning is not just another buzzword; it’s the engine driving modern AI’s most impactful innovations. While many programmes gloss over the technical complexities, we believe that true leadership in the age of AI requires a deep understanding of the algorithms and data driven methods that power these technologies.
This focus on machine learning, rather than just the surface-level aspects of AI, is what sets our programme apart
Key Learnings
- Foundations of Machine Learning: The basics of machine learning, including deep dives into supervised and unsupervised learning problems relevant to finance and insurance, as well as advanced deep learning methods and large language models.
- Industry-Relevant Applications: The full value chain of a machine learning project, from from designing IT architecture and machine learning pipelines, to model deployment, and maintenance.
- Responsible AI: The most relevant interpretability methods and strategies to build trust in machine learning-powered services, ensuring their responsible use. This includes navigating the regulatory landscape that governs the design, deployment, and maintenance of AI technologies.
- Effective Innovation: The typical phases of an innovation process within a corporate or fintech environment.
How the Programme works
Through engaging lectures, hands-on projects, and direct interactions with industry experts, you will understand how leading banks, insurance companies, and fintechs are applying machine learning to enhance risk management, detect fraud, and solve complex efficiency challenges. You will emerge with a solid grasp of what of what machine learning and AI really are and can offer to the financial services industry, enabling you to make a meaning impact in integrating machine learning technologies within your organization.
Overall the programme is structured over 9 months: From March to November
Format: Lectures with exercises (March, April, May)
Workload: 8 Fridays 6h in class, 4-6h preparation time per week
About: You will gain a solid foundation in the fundamentals of machine learning, including key concepts, models, algorithms and practical applications to develop the skills required to train and evaluate machine learning models for different real-world tasks .
- Key Concepts: Mathematical background for machine learning, the theory of statistical learning, machine learning methods, including supervised and unsupervised modeling and deep learning.
- Skills-Building-Activities: To deepen your understanding, you will work on in-class exercises and explore additional materials through self-paced learning. Continuous feedback from ETH faculty will guide and support you throughout your learning journey.
- Assessment: You will undertake two projects that apply machine learning techniques to address practical challenges in finance and insurance. In each project, you will articulate the theoretical foundations, demonstrate their application to real-world scenarios, and develop code to implement the solutions.
+ Additional 2-Day Bootcamp on «Fundamentals of Generative AI»
About: This workshop offers an in-depth exploration of the theory and fundamentals of generative AI, equipping you with a strong foundation in the algorithms, models, and key concepts behind this advanced technology. You will learn about the mathematical principles underlying generative models and participate in hands-on coding sessions to build and experiment with generative AI systems.
Format: Full-Day Workshop with Lectures, Case Work and Guest Speakers
Workload: 3 Days in May/June, 6-8h preparation time
About: You will gain a multidisciplinary understanding of AI ethics, which guides the responsible design of AI systems in society. You will engage in dynamic discussions on key ethical questions, the complexities of AI-assisted decision-making, and explore critical issues like machine learning fairness, transparency, and explainability through hands-on coding exercises. Finally, you will dive into how regulations, including the EU AI Act, address the design, development, and maintenance of AI systems, equipping you with insights into the evolving landscape of AI governance
Format: Full-Day Workshops, Case Work, Guest Speakers & Company Visits
Workload: 5 x 2 Days in June/July/September, each with 4-6h preparation time
About: You will get exposure to real-world case studies and projects in finance and insurance where ML techniques are tested and successfully applied. Gain insights and understanding of the overall system landscape and pipeline in which a machine learning model is embedded.
- Cases in Machine Learning in Finance I
- Cases in Machine Learning in Finance II
- Cases in Machine Learning in Insurance I
- Cases in Machine Learning in Insurance II
- Cases in Fintech and Startups
- Study Trip
Structured as an interactive workshop on Fridays and Saturdays. Students select 3 out of 4 workshops offered between June, July and September. Workshops take place at ETH or at corporate facilities.
Format: Individual Project Work over 4-6 weeks
Workload: Overall 30h
Innovation Workshop: One-Day Workshop on Innovation, Ideation, Growth
About: Spurred on by the learnings achieved through Blocks I-III, Block IV will allow you to advance your machine learning skills through an individual project over 4-6 weeks. Whether you focus on machine learning modeling, the risks of ML-powered services, or the design of innovative applications in finance and insurance, your project will be tailored to your interests and career goals. This capstone project enables you to apply your knowledge to real-world challenges, showcasing your ability to drive innovation in your field.
