Found 12 relevant results in 1.99s where lecturer="Valentina Boeva"
Students will study bioinformatic concepts in the areas of metagenomics, genomics, transcriptomics, proteomics, biological networks and biostatistics. Through integrated lectures, practical hands-on exercises and project work, students will also be trained in analytical and programming skills to meet the emerging increase in data-driven knowledge generation in biology in the 21st century.
The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
This laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications.
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.
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.
Seminal and recent papers in machine learning are presented and discussed.
The course reviews solutions provided by machine learning to the most challenging questions in human genomics.
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.