Microbiome Summer School 2017

Introduction to Machine Learning

View project on GitHub

Introduction to machine learning

Table of contents

  1. Introduction
    1. Overview of our toolbox
    2. Getting started
    3. Objectives
  2. The basics
    1. Type of learning problems
    2. Typical experimental protocol
    3. Cross-validation
    4. Assessing the accuracy of a model
    5. Interpretable vs black-box models
  3. Application: peptide protein binding affinity prediction
  4. Application: predicting antibiotic resistance
  5. Conclusion


For any questions or comments, please contact Alexandre Drouin or François Laviolette.


This tutorial was created for presentation at the 2017 Microbiome Summer School (Québec City, Canada).

Shortened URL: http://git.io/mltutorial