Some Atypical Applications of Typical Machine Learning Algorithms

A workshop highlighting how to resourcefully use algorithms, run by the world-renowned data scientist, Kirk Borne. Kirk will present a variety of atypical use cases and applications (in science and in business) of some typical textbook machine learning algorithms, including regression, clustering analysis, association mining, time series analysis, and network analysis.

Who it’s for: Anyone interested in machine learning, analytics and data science; Chief Digital Officers, Intrapreneurs, Chief Innovation Officers, Data Scientists, Developers. The audience does not need advanced knowledge or experience, but my use cases will hopefully be interesting and useful even for those people too. Also, the attendees do not need to download or to prepare anything in advance. The applications that I discuss will cover different industries (healthcare, science, marketing, business, cyber), and therefore the applications are relevant to any industry, since the applications cover the basic types of discovery from data science: Class Discovery, Trend/Correlation Discovery, Novelty (Anomaly) Discovery, and Link (Association) Discovery --- those are applicable everywhere!

Key skills and abilities to discover: Design thinking, critical thinking, and finding data-driven ways to find solutions. Key question: “how does data drive the design?”

“It’s really for anyone keen on translating data into action” –Kirk Borne

Dr. Kirk Borne is a data scientist and an astrophysicist. He is Principal Data Scientist in the Strategic Innovation Group at Booz-Allen Hamilton since 2015. He was Professor of Astrophysics and Computational Science in the George Mason University (GMU) School of Physics, Astronomy, and Computational Sciences during 2003-2015. He served as undergraduate advisor for the GMU Data Science program and graduate advisor to students in the Computational Science and Informatics PhD program. Prior to that, he spent nearly 20 years supporting NASA projects, including NASA's Hubble Space Telescope as Data Archive Project Scientist, NASA's Astronomy Data Center, and NASA's Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope (LSST), for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach.