Bayes Centre

Explainable Machine Learning: A Practical Introduction

This online credit-bearing course is aimed at aspiring data scientists and software engineers, who have technical backgrounds and roles in organisations involving or related to machine learning and artificial intelligence.

Course Summary

Machine learning provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with significant challenges: how do we understand the decisions suggested by these systems in order that we can trust them? In this course, we study, survey and distil the results and observations from the literature.    

The course focuses on understanding and applying XAI techniques and judging their shortcomings. The course aims to focus on methodological thinking and understanding principles for the application of post-hoc explainability. 

The course helps them understand and apply innovative and state-of-the-art techniques on explaining machine learning models so as to lead and manage the use of machine learning in their work place. Explainability will likely be a key component for encouraging major organisational changes via the wider deployment of artificial intelligence technologies, and this course will provide strong foundations for navigating and critiquing developments in the area.

Course Delivery Information

Start Date:

To be confirmed

Course Duration: 3 months
Total Hours: 100 (Lecture Hours 10; Tutorial Hours, including assignments 8; Feedback Hours, including assignments 8; Independent Study Hours 74)
Method of Assessment: Coursework 100%  (2 coding assignments and a final coding project) 
Level:  This is an intermediate Masters-level course (SCQF Level 11). It develops your skills and/or provides a broad understanding of the subject in some detail - some foundational knowledge is required. Please see the entry requirements for further details. Masters-level courses are relatively intensive and require independent learning, critical thinking, analysis and reflection.

Applications for 2021/22 will open Autumn 2021.

Entry Requirements

This standalone course expects users to have some prior experience in machine learning and programming in python, but does not require them to have taken any other course per se.  (Prior experience in machine learning implies some knowledge of the underlying concepts for one or more standard prediction/classification models e.g., random forests, training, feature selection, and evaluation. Mathematical knowledge of these concepts is not assumed but it is helpful to be able to read machine learning-related mathematical notation.) 

If you are unsure whether you meet these criteria please send your CV to bayes-training@ed.ac.uk

Check whether your international qualifications meet our general entry requirements:

English Language Requirements

You must be comfortable studying and learning in English if it is not your first language.