Introduction to Machine Learning with Python

Abstract

This 2-day course will introduce participants to common ML algorithms and teach how to apply them to omics data in extensive practical sessions. The practical sessions will be conducted in Python3 based on the widely applied scikit-learn ML framework.

Learning Objectives

At the end of the course, the participants are expected to:

    Understand the ML taxonomy and the commonly used machine learning algorithms for analysing “omics” data
    Understand differences between ML approaches and in which situations they can be applied
    Understand and critically evaluate applications of ML in omics studies
    Learn how to implement common ML algorithms using the scikit-learn Python framework
    Interpret and visualize the results obtained from ML analyses

Content

The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML methods and processes, as well as the practical skills in applying them to real world problems using publicly available biological or medical data sets.

Location

• ETH Hönggerberg
  HCI G2

Target audience

Language