Explainable Machine Learning (INFD11019)

Explainable Machine Learning (INFD11019)
Media for the School of Informatics course Explainable Machine Learning: A Practical Introduction (INFD11019).

Artificial intelligence (AI) 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 focus specifically on data driven methods - machine learning (ML) and pattern recognition models in particular - so as to survey and distill the results and observations from the literature. The purpose of this course is to provide and explore the principles and practice of enabling explainability in machine learning models. The course builds a narrative around a putative data scientist, and discusses how she might go about explaining her models by asking the right questions.

Here you will find the pre-recorded lectures, recordings of the interactive online sessions and tutorial sessions. 

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 Public, Restricted

34 Media
8 Members
Managers:
 Auste Simkute
 Fazl Barez
 Andreas Bueff
 Pavlos Andreadis
 Vaishak Belle
 Jonathan Feldstein
 Giannis Papantonis
 Auste Simkute
Appears In: