Teaching
Courses offered in Autumn 2025
Data Science Lab - 263-3300-00L
| Lecturers | A. Ilic, V. Boeva, A. Devos, J. Vogt, F. Yang |
| Semester | Autumn 2025 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Topics in Medical Machine Learning - 263-5100-00L
| Lecturers | G. Rätsch, J. Vogt |
| Semester | Autumn 2025 |
| Language | English |
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.
Courses offered in Spring 2025
Data Science for Medicine - 252-0868-00L
| Lecturers | J. Vogt, V. Boeva, G. Rätsch |
| Semester | Spring 2025 |
| Language | English |
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.
Machine Learning for Health Care - 261-5120-00L
| Lecturers | J. Vogt, V. Boeva, G. Rätsch |
| Semester | Spring 2025 |
| Language | English |
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.
Data Science Lab - 263-3300-00L
| Lecturers | A. Ilic, V. Boeva, A. Devos, J. Vogt, F. Yang |
| Semester | Spring 2025 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Courses offered in Autumn 2024
Data Science Lab - 263-3300-00L
| Lecturers | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Autumn 2024 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Topics in Medical Machine Learning - 263-5100-00L
| Lecturers | G. Rätsch, J. Vogt |
| Semester | Autumn 2024 |
| Language | English |
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.
Courses offered in Spring 2024
Data Science for Medicine - 252-0868-00L
| Lecturers | J. Vogt, V. Boeva, G. Rätsch |
| Semester | Spring 2024 |
| Language | English |
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.
Doctoral Seminar Machine Learning - 252-0945-18L
| Lecturers | T. Hofmann, V. Boeva, J. M. Buhmann, R. Cotterell, N. He, G. Rätsch, M. Sachan, J. Vogt, F. Yang |
| Semester | Spring 2024 |
| Language | English |
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.
Machine Learning for Health Care - 261-5120-00L
| Lecturers | J. Vogt, V. Boeva, G. Rätsch |
| Semester | Spring 2024 |
| Language | English |
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.
Data Science Lab - 263-3300-00L
| Lecturers | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Spring 2024 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Courses offered in Autumn 2023
Doctoral Seminar Machine Learning - 252-0945-17L
| Lecturers | N. He, V. Boeva, J. M. Buhmann, R. Cotterell, T. Hofmann, A. Krause, G. Rätsch, M. Sachan, J. Vogt, F. Yang |
| Semester | Autumn 2023 |
| Language | English |
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.
Data Science Lab - 263-3300-00L
| Lecturers | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Autumn 2023 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Topics in Medical Machine Learning - 263-5100-00L
| Lecturers | G. Rätsch, J. Vogt |
| Semester | Autumn 2023 |
| Language | English |
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.
Courses offered in Spring 2023
Machine Learning for Health Care - 261-5120-00L
| Lecturers | V. Boeva, J. Vogt, M. Kuznetsova |
| Semester | Spring 2023 |
| Language | English |
During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical 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 biomedicine, discuss the main challenges they present and their current technical solutions.
Data Science for Medicine - 252-0868-00L
| Lecturers | J. Vogt, V. Boeva, M. Kuznetsova |
| Semester | Spring 2023 |
| Language | English |
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.
Doctoral Seminar Machine Learning - 252-0945-16L
| Lecturers | N. He, V. Boeva, J. M. Buhmann, R. Cotterell, T. Hofmann, A. Krause, M. Sachan, J. Vogt, F. Yang |
| Semester | Spring 2023 |
| Language | English |
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.
Data Science Lab - 263-3300-00L
| Lecturers | A. Ilic, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Spring 2023 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Courses offered in Autumn 2022
Data Science Lab - 263-3300-00L
| Lecturers | C. Zhang, V. Boeva, R. Cotterell, A. Ilic, J. Vogt, F. Yang |
| Semester | Autumn 2022 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Topics in Medical Machine Learning - 263-5100-00L
| Lecturers | G. Rätsch, J. Vogt |
| Semester | Autumn 2022 |
| Language | English |
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.
Courses offered in Spring 2022
Data Science Lab - 263-3300-00L
| Lecturers | C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Spring 2022 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Machine Learning for Health Care - 261-5120-00L
| Lecturers | V. Boeva, G. Rätsch, J. Vogt |
| Semester | Spring 2022 |
| Language | English |
During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical 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 biomedicine, discuss the main challenges they present and their current technical solutions.
Data Science for Medicine - 252-0868-00L
| Lecturers | J. Vogt, V. Boeva, G. Rätsch |
| Semester | Spring 2022 |
| Language | English |
The course will start with a general introduction to ML, where we will cover supervised and unsupervised learning techniques, as for example classification and regression models, feature selection and preprocessing of data, clustering and dimensionality reduction techniques. After the introduction of the basic methodologies, we will continue with the most relevant applications of ML in medicine, as for example dealing with time series, medical notes and medical images.
Courses offered in Autumn 2021
Advanced Topics in Machine Learning - 252-5051-00L
| Lecturers | J. M. Buhmann, R. Cotterell, J. Vogt, F. Yang |
| Semester | Autumn 2021 |
| Language | English |
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.
Data Science Lab - 263-3300-00L
| Lecturers | C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Autumn 2021 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Courses offered in Spring 2021
Machine Learning for Health Care - 261-5120-00L
| Lecturers | V. Boeva, G. Rätsch, J. Vogt |
| Semester | Spring 2021 |
| Language | English |
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.
Data Science for Medicine - 252-0868-00L
| Lecturers | J. Vogt, V. Boeva, G. Rätsch |
| Semester | Spring 2021 |
| Language | English |
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.
Data Science Lab - 263-3300-00L
| Lecturers | C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Spring 2021 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Courses offered in Autumn 2020
Advanced Topics in Machine Learning - 252-5051-00L
| Lecturers | J. M. Buhmann, G. Rätsch, J. Vogt, F. Yang |
| Semester | Autumn 2020 |
| Language | English |
The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.
Data Science Lab - 263-3300-00L
| Lecturers | C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang |
| Semester | Autumn 2020 |
| Language | English |
In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc.
Courses offered in Spring 2020
Digital Medicine II - 252-0868-00L
| Lecturers | J. Vogt; G. Rätsch; N. Davidson |
| Semester | Spring 2020 |
| Language | English |
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.
Machine Learning for Health Care - 261-5120-00L
| Lecturers | G. Rätsch, J. Vogt, V. Boeva |
| Semester | Spring 2020 |
| Language | English |
The course "Machine Learning in Health Care" critically reviews central problems in Health Care and discusses the technical foundations and solutions for these problems. Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.