Found 9 relevant results in 2.06s where lecturer="Fan Yang"
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.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
This course is aimed at advanced master and doctorate students who want to conduct independent research on theory for modern machine learning (ML). It teaches standard methods in statistical learning theory commonly used to prove theoretical guarantees for ML algorithms. The knowledge is then applied in independent project work to understand and follow-up on recent theoretical ML results.
The course introduces the foundations of learning and making predictions based on data.
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