With the increased focus within the discipline of eDiscovery on Technology-Assisted Review, three references are provided to help legal professionals establish a solid base of definitional and contextual information for considering machine learning.
Reference #1: Book: New Advances in Machine Learning. Chapter: Types of Machine Learning Algorithms. Author: Taiwo Oldipupo Ayodele (University of Portsmouth, United Kingdom).
Reference #2: Video: Lectures on Machine Learning. Lecturer: Andrew Nq (Director , Stanford Artificial Intelligence Lab, Stanford University).
Available via Video Series Link: https://class.coursera.org/ml/lecture/preview
Reference #3: Video Lectures on Machine Learning. Lecturer: Pedros Domingos (Professor of Computer Science & Engineering, University of Washington.
Available via Video Series Link: https://class.coursera.org/machlearning-001/lecture/preview
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Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for software and services in the electronic discovery market for the years between 2015 and 2020.
When Maura Grossman speaks, people listen. In 2011, she was already known as a leading e-discovery attorney and litigator. But her influence exploded when she released research with co-author Gordon Cormack, a computer science professor at the University of Waterloo in Ontario, that concluded software using predictive-coding technology can do as good a job of sifting through documents as human reviewers.
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The Actionable Intelligence (@ActionableINT) Weekly "Quick 10" Corporate Risk Review provides in-house counsel with a weekly overview of ten significant...