The course will give students an overview of selected topics in advanced machine learning that are currently subjects of active research. The course concludes with a final project.

In this class, we bring together data science applicationsprovided by ETH researchers outside computer science andteams of computer science master's students. Two to threestudents will form a team working on data science/machinelearning-related research topics provided by scientists ina diverse range of domains such as astronomy, biology,social sciences etc.

In this class, we bring together data science applications provided by academic & industry stakeholders with teams of computer science master's students. Teams of students work on data science/machine learning-related research topics. Teams consist of two to three students, depending on the number of projects. Projects are collected by the lecturers and made available to choose from at the start.

This course focuses on the engineering principles and practices required to develop and optimize large-scale AI systems. Studentswill gain hands-on experience with high-performance computing (HPC) infrastructures, emphasizing the deployment and scaling of AI models on advanced GPU clusters.

In this course dedicated to digital innovations, we will bust the most stubborn myths around AI software patents such as “Software/AI isn’t patentable”, “AI patents are useless because you can’t figure out if they are infringed”, and many others. We will look at how AI and software start-ups can use patents to create a strong IP position in a scalable way.

This course provides theoretical and practical insights into technology entrepreneurship. It focusses on the process of building new ventures from the idea to successfully scaling its business operations.! Please note, that in-person attendance is mandatory during the course times. For group work we also encourage you to work together in-person, however your group may decide individually.