Statistics and Machine Learning for Life Sciences

Abstract

Statistics are an integral aspect of the domains of life sciences that rely on quantitative methodologies and are essential for understanding the relationships between measurements and variables. Machine learning (ML) also assists in understanding complex datasets and can be helpful in mining large biological datasets. This course guides participants in the exploration of statistical modeling and relates and contrasts them with machine learning approaches.

Learning Objectives

At the end of the course, the participants will be able to:

• perform linear and logistic regressions, and critically evaluate their result
• describe the general Machine Learning data analysis pipeline
• implement a classification task and appraise the resulting model
• compare the statistical and Machine Learning approaches to regression and choose the most appropriate for their question.

Content

A particular focus will be given to evaluating the relevance of the produced models and their interpretation in order to provide new biological knowledge.

Location

• ETH Hönggerberg
  HCI

Target audience

Language

Comment

The course is targeted to life scientists who are already familiar with the Python programming language and who have basic knowledge on statistics. The competences and knowledge levels required correspond to those taught in courses such as: First Steps with Python in Life Sciences and Introduction to statistics with R.