Call for Speakers: Machine Learning Week 2021
For 2021, Predictive Analytics World in North America will continue its growth trajectory by once again bringing together all industry-specific PAW events for the fourth Machine Learning Week – May 24 – 28, 2021. Note, due to the difficulty in predicting the course of the covid 19 pandemic and in light of travel restrictions in place in many companies the event will be virtual, livestreamed. We’ll be spreading the core conference over five half days rather than two full days. Sessions will take place between 8:00am and 1:00pm Pacific Time, Monday – Friday, May 24-28. The wide range of jointly scheduled events includes: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, and Deep Learning World
SPEAKER PROPOSALS ARE NOW BEING ACCEPTED FOR ALL MACHINE LEARNING WEEK MAY 2021 VIRTUAL CONFERENCES
To apply, please read the following instructions and then click on the Call for Speakers Form underneath.
DEADLINE for submission is November 6, 2020 – but the earlier the better for consideration.
Maximize Your Chances of Being Accepted by Following these Recommendations:
All speakers: Please read this call for speakers in its entirety before proceeding to the speaker proposal form (below).
Software vendors: If you are a software vendor, read this restriction on speaking.
Join Machine Learning Week to share how predictive analytics and machine learning deliver a business, operational or clinical impact for your organization. Presenting at PAW is a fulfilling way to engage with the leading cross-vendor community of the field, and provides complimentary registration/access to the PAW event.
Join an elite crowd. Prior Machine Learning Week speakers have included:
- Uber: Mike Tamir, Head of Data Science, ATG
- Caterpillar: Morgan Vawter, Chief Analytics Director
- Dell EMC: Theresa Kushner, Sr VP, Performance Analytics Group
- Capital One: Kate Highnam, Machine Learning Engineer
- Elder Research: John Elder, Founder & Chair
- Northern Trust: Andy Curtis, Senior VP
… plus leading practitioners presenting on deployment case studies from Becker College, Central Pacific Bank, Cisco, Comcast, Google, Hitachi, IBM, John Hancock, Lyft, Northwestern Mutual, Quicken Loans, Seagate, Shell, Turner, Twitter, Verizon, and more.
This event covers machine learning, which is essentially synonymous with predictive analytics. Although “machine learning” used to be common only within the walls of research labs, it’s now also used more and more in the context of commercial deployment. Whichever term you prefer, PAW covers technology that learns from data to predict or infer an unknown, including decision trees, logistic regression, neural networks, and many other methods.
The premier cross-vendor machine learning event focused on commercial/operational deployment, Predictive Analytics World is the only conference of its kind. PAW sessions and content reach:
- Across applications – For what purpose is machine learning deployed?
- Across industries – Where is machine learning deployed?
- Across vendors of solutions and software – How is machine learning deployed?
As a vendor-neutral event, PAW’s core program is booked exclusively with enterprise practitioners, thought leaders and adopters, with no predictive analytics software vendors eligible to present or co-present. If you represent an analytics software vendor, a vendor of a software solution designed to support the development or deployment of analytics (regardless of whether the solution itself generates the analytical model or analytical component to be deployed), or a company with webpages or materials that gives the clear impression you sell an analytics software solution, then you are not eligible to submit the speaker proposal form below. As an alternative, you are encouraged to consider Becoming a Sponsor, and/or to suggest your clients submit a proposal to speak (point them to this web page).
Present Your Case Studies
Predictive Analytics World provides speakers the opportunity to present machine learning case studies, deployment successes and lessons learned. At this event, potential consumers of machine learning witness proof demonstrating it’s more than just a bunch of great ideas – machine learning is actively applied to optimize many business functions across industry verticals. And machine learning practitioners have the opportunity to gain from the lessons you’ve learned, whether by serendipity, or – more likely – the hard way.
Evaluation – how well did it work? Case study proposals will be given highest consideration if specific measurements of deployment performance are included, especially when measured in comparison to a control group.
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