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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An abridged look at the business of eDiscovery mergers, acquisitions, and investments. The presented listing highlights key industry business moves by sharing the announcement date, acquired company, acquiring or investing company, and acquisition amount (if known) of significant eDiscovery-related mergers, acquisitions, and investments.
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 2016 and 2021.
One of the core purposes of all of the Tracks is to demonstrate the robustness of core retrieval technology. Moreover, one of the primary goals of TREC is: [T]o speed the transfer of technology from research labs into commercial products by demonstrating substantial improvements in retrieval methodologies on real-world problems.
The proceedings of the TREC Total Recall Track have been published by the National Institute of Standards and Technology. The purpose of track was to investigate methods and technologies to find, as nearly as possible, all documents in a collection that satisfy specific criteria, with reasonable effort.
Best Practices for eDiscovery Searching: A Continuing Legal Education (CLE) On-Demand Presentation (1.0 Hour) prepared and presented by CloudNine. This CLE-approved webcast session will cover goals for effective searching, what to consider prior to collecting ESI that will be subject to search, mechanisms for culling prior to searching, mechanisms for improving search recall and precision, challenges to effective searching and recommended best practices for searching and validating your search results to ensure effective search results.
This is the e-Discovery Team’s training course on how to do TAR (Technology Assisted Review). What TAR really means is electronic document review enhanced by active machine learning, a type of specialized Artificial Intelligence. Our method of AI-enhanced document review is called Hybrid Multimodal IST Predictive Coding 4.0. The Course is composed of sixteen classes.
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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...