252-0868-00L Data Science for Medicine (Spring 2021)

Semester Spring Semester 2021
Lecturers Julia Vogt; Valentina Boeva; Gunnar Rätsch
Periodicity Yearly block course
Language of instruction English

Abstract
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.

Objective
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.

Content
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.

Location

Date Time Room
March 29-April 1st 08-18 HG D 1.2 (Virtual only)
April 12-16 08-18 HG D 1.2 (Virtual only)

Project Details

No public learning materials available
More details are available in the Course Catalog