Teaching
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 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.