An Introduction to Machine Learning Interpretability
Overview & Synopsis
Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.
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Document Information
| Document Title | An Introduction to Machine Learning Interpretability |
|---|---|
| Author / Organization | Patrick Hall and Navdeep Gill |
| Subject & Discipline | Machine Learning, Deep Learning and Neural Networks, Artificial Intelligence, Statistics, Mathematical Statistics, and SAS Programming, Probability and Stochastic Processes |
| Categories | Machine Learning |
| ISBN | 978-1098115456 |
| Resource Verification | PDF Unavailable at Remote Host |
| Alternative Mirrors & Links | |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Language | ENGLISH |
| Catalog ID | DOC-9733 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |
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