November 13-14 2023
10:00 - 17:00
Instructors: Sam Haynes, Ben King
Helpers: Giacomo Peru, Rosie Eccleston, Mario Antonioletti, Hugh Warden
Introduction to Machine Learning with Python
This workshop comprises four lessons on applied machine learning in Python using health data. Lessons take participants through a typical pipeline for prediction, covering key concepts in preparing data, training models, and evaluating performance. We introduce models including decision trees and neural networks and highlight key issues in their responsible use. Prior knowledge of Python (for example, gained through a Carpentries course) is beneficial, but not required. It was developed by ED-DaSH and now adopted into the Edinburgh Carpentries DUSC programme.
Ed-DaSH
Ed-DaSH is a Data Science training programme for Health and Biosciences. The team has developed workshops using The Carpentries platform on the following topics. See workshops for dates and registration details. All workshops will be delivered remotely.
Where: Digital Scholarship Centre located on Floor 6 of UoE Library on November 13th, Research Suite located on Floor 6 UoE Library on November 14th. Get directions with OpenStreetMap or Google Maps.
When: November 13-14 2023. Add to your Google Calendar.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).
Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:
Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.
Contact: Please email lucie.woellenstein@ed.ac.uk for more information.
Roles: To learn more about the roles at the workshop (who will be doing what), refer to our Workshop FAQ.
Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.
We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.
The lesson taught in this workshop is being piloted and a precise schedule is yet to be established.
| Lessons | |
| Introduction to machine learning | Introduction |
| Data preparation | |
| Learning | |
| Modelling | |
| Validation | |
| Evaluation | |
| Lessons | |
| Responsible machine learning | Introduction |
| Tasks | |
| Data | |
| Fairness | |
| Dataset shift | |
| Explainability | |
| Attacks | |
| Lessons | |
| Tree models | Introduction |
| Decision trees | |
| Variance | |
| Boosting | |
| Bagging | |
| Random forest | |
| Evaluation | |
To participate in a workshop, you will need access to software described here. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
Please check the "Setup" page of [the lesson site]() for instructions to follow to obtain the software and data you will need to follow the lesson.