Courses given by members of the BMI Lab

Courses in the Spring Semester 2024

261-5113-00L Computational Challenges in Medical Genomics

Link: Course web page, Course Catalogue ETH 

The topics covered in this seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.

Courses in the Autumn Semester 2023

263-5056-00L Applications of Deep Learning on Graphs

Link: Course Web Page, Course Catalogue ETH 

Graphs are an incredibly versatile abstraction to represent arbitrary structures such as molecules, relational knowledge, or social and traffic networks. This course provides a practical overview of deep (representation) learning on graphs and their applications.

261-5112-00L Algorithms and Data Structures for Population Scale Genomics HS23

Link: Course web page, Course Catalogue ETH 

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 block 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 Semester 2023

261-5113-00L Computational Challenges in Medical Genomics

Link: Course web page, Course Catalogue ETH 

The topics covered in this seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.

Courses in the Autumn Semester 2022

261-5112-00L Algorithms and Data Structures for Population Scale Genomics

Link: Course web page, Course Catalogue ETH 

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 block 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 Semester 2022

261-5120-00L Machine Learning for Health Care

Link: Course web page, Course Catalogue ETH 

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.

263-0008-00L Computational Intelligence Lab

Link: Course Catalogue ETH 

The goal of the Computational Intelligence Lab is to enable master level students to connect their mathematical background in linear algebra, analysis, probability, and optimization with their basic knowledge in machine learning and their general skill set in Computer Science to gain a deeper understanding of models and tools of great practical impact.

261-5113-00L Computational Challenges in Medical Genomics

Link: Course web page, Course Catalogue ETH 

The topics covered in this seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.

252-0868-00L Data Science for Medicine

Link: Course web page, Course Catalogue ETH 

During the last few years, we have observed a rapid growth of Machine Learning (ML) in Medicine. 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, discuss the main challenges they present and their current technical solutions, and work on practical projects to solve medical problems with the help of ML.

261-5112-00L Algorithms and Data Structures for Population Scale Genomics

Link: Course web page, Course Catalogue ETH 

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 block 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 Autumn Semester 2021

261-5100-00L Computational Biomedicine

Link: Course web page, Course Catalogue ETH 

Courses in the Spring Semester 2021

261-5113-00L Computational Challenges in Medical Genomics

Link: Course web page, Course Catalogue ETH 

The topics covered in this seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.

261-5120-00L Machine Learning for Health Care

Links: Course web page, Course Catalogue ETH

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.

252-0868-00L Data Science for Medicine

Links: Course web page, Course Catalogue ETH 

During the last few years, we have observed a rapid growth of Machine Learning (ML) in Medicine. 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, discuss the main challenges they present and their current technical solutions, and work on practical projects to solve medical problems with the help of ML.

Courses in the Autumn Semester 2020

261-5100-00L Computational Biomedicine

Links: Course web page, Course Catalogue ETH

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.

261-5112-00L Algorithms and Data Structures for Population Scale Genomics

Link: Course web page, Course Catalogue ETH 

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.

551-1299-00L Introduction to Bioinformatics

Link: Course web page, Course Catalogue ETH 

This course introduces principle concepts, the state-of-the-art and methods used in some major fields of Bioinformatics. Topics include: genomics, metagenomics, network bioinformatics, and imaging. Lectures are accompanied by practical exercises that involve the use of common bioinformatic methods and basic programming.

Courses in the Spring Semester 2020

261-5113-00L Computational Challenges in Medical Genomics

Link: Course web page, Course Catalogue ETH 

The topics covered in this seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.

261-5120-00L Machine Learning for Health Care

Links: Course web page, Course Catalogue ETH

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.

252-0868-00L Digital Medicine II

Links: Course web page, Course Catalogue ETH

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. The course "Digital Medicine II" 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.

Courses in the Autumn Semester 2019

261-5100-00L Computational Biomedicine

Links: Course web page, Course Catalogue ETH

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.

261-5112-00L Advanced Approaches for Population Scale Compressive Genomics

Link: Course web page, Course Catalogue ETH 

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.

551-1299-00L Introduction to Bioinformatics

Link: Course web page, Course Catalogue ETH 

This course introduces principle concepts, the state-of-the-art and methods used in some major fields of Bioinformatics. Topics include: genomics, metagenomics, network bioinformatics, and imaging. Lectures are accompanied by practical exercises that involve the use of common bioinformatic methods and basic programming.

Courses in the Spring Semester 2019

261-5120-00L Machine Learning for Health Care

Links: Course web page, Course Catalogue ETH

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.

261-5113-00L Computational Challenges in Medical Genomics

Link: Course web page, Course Catalogue ETH 

The topics covered in this seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.

Courses in the Autumn Semester 2018

261-5100-00L Computational Biomedicine

Links:
Course web page, Course Catalogue ETH

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.

252-5051-00L Advanced Topics in Machine Learning

Links: Course web page, Course Catalogue ETH

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.

261-5112-00L Advanced Approaches for Population Scale Compressive Genomics

Link: Course web page, Course Catalogue ETH 

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.

551-1299-00L Introduction to Bioinformatics

Link: Course web page, Course Catalogue ETH 

This course introduces principle concepts, the state-of-the-art and methods used in some major fields of Bioinformatics. Topics include: genomics, metagenomics, network bioinformatics, and imaging. Lectures are accompanied by practical exercises that involve the use of common bioinformatic methods and basic programming.

Courses in the Spring Semesters 2018

261-5120-00L Computational Biomedicine II

Links: Course web page, Course Catalogue ETH

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

261-5100-00L Computational Biomedicine

Links:
Course web page, Course Catalogue ETH 

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.

252-5051-00L Advanced Topics in Machine Learning

Links:
Course webpage, Course Catalogue ETH 

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.