Courses given by members of the BMI Lab

Courses in the Autumn Semester 2018

Links:
Course web pageCourse Catalogue ETH

Semester Autumn Semester 2018
Lecturers G. Rätsch
Periodicity yearly course
Language of instruction 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.

Links: Course Catalogue ETH

Semester Autumn Semester 2018
Lecturers J. M. Buhmann, A. Krause, G.Rätsch
Periodicity yearly course
Language of instruction 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.

Link: Course web pageCourse Catalogue ETH 

Semester Autumn Semester 2018
Lecturers A. Kahles
Periodicity yearly course
Language of instruction English

Research in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherent complexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions. This interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it.

Courses in the Spring Semesters 2018

Links: Course web pageCourse Catalogue ETH

Semester Spring Semester 2018
Lecturers G. Rätsch
Periodicity yearly course
Language of instruction English

The course "Computational Biomedicine" critically reviews central problems in Biomedicine 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.

Courses in the Autumn Semester 2017

Links:
Course web pageCourse Catalogue ETH 

Semester Autumn Semester 2017
Lecturers G. Rätsch
Periodicity yearly course
Language of instruction 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.

Links:
Course webpageCourse Catalogue ETH 

Semester Autumn Semester 2017
Lecturers J. M. Buhmann, T. Hofmann, A. Krause, G.Rätsch
Periodicity yearly course
Language of instruction 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.