In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.

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.

Machine Learning (ML) methods have shown to have a profound impact in medical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in medicine, and work on practical projects to solve medical problems with the help of ML.

The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems.

This seminar discusses recent relevant contributions to the fields of medical machine learning and related areas. Each participant will hold a presentation and lead the subsequent discussion.