Renowned Expert Dean Abbott Returns to MLW
Machine Learning Week in Las Vegas May 31 - June 4 is pleased to announce the return of Dean Abbott this year to the event lineup. Don't miss this opportunity to learn from this internationally recognized expert and practitioner during his session, as well as his training workshop, which takes place the day after the main two-day conference.
Meet Dean Abbott
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Dean Abbott
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Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics in San Diego, California. Mr. Abbott is an internationally recognized machine learning and predictive analytics expert with over three decades of experience applying advanced algorithms to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a highly-regarded and popular speaker at Machine Learning conferences and meetups, and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate and UCSD Data Mining and Advanced Analytics Certificate programs. He has a B.S. in Mathematics of Computation from Rensselaer Polytechnic Institute (1985) and a Master of Applied Mathematics from the University of Virginia (1987). |
Abbott’s Session – at PAW Business: June 2, 2020
Using Perturbation Experiments to Interpret Complex Models
This talk – part of Predictive Analytics World for Business – will describe the use of random permutation to uncover and describe model input and model prediction sensitivities. Techniques such as Breiman’s “permutation importance” and the use of bootstrap sampling to uncover sensitivities will be discussed and will be applied to models built from data drawn from customer analytics. Click here for more details.
Abbott’s Training Workshop: June 4, 2020
Ensemble Models: Supercharging Machine Learning
Are model ensembles an algorithm or an approach? How can one understand the influence of key variables in the ensembles? Which options affect the ensembles most? This workshop dives into the key ensemble approaches, including Bagging, Boosting, Random Forests, and Stochastic Gradient Boosting. Attendees will learn “best practices” and attention will be paid to learning and experiencing the influence various options have on ensemble models so that attendees will gain a deeper understanding of how the algorithms work qualitatively and how one can interpret resulting models. Attendees will also learn how to automate the building of ensembles by changing key parameters. Click here for more details.
View Full Agenda of MLW 2020 Here
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Carlos Martinez, President, Innate Intelligence, Inc.
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