Atypical Applications of Typical Machine Learning Algorithms
Atypical Applications of Typical Machine Learning Algorithms


How could a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? How can a mathematical concept from the 18th century be used to estimate the mass density of galaxies across the Universe? How could a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a F1 race from the 1950's inspire one of the greatest machine learning use cases for the Internet of Things? This workshop will answer these questions, and more, by presenting several examples of typical algorithms that were adopted for specific use cases or application domains, then showing how each one can be adapted to an atypical (often mind-bending) use case, producing significantly surprising results in some other domain. These exercises serve to demonstrate how data scientists can create even more value, beyond that which is expected, from our data sets and our algorithmic talents.

Session Outline
1. The Two Most Important Things in Data Science: The Data and the Science
2. The Case for Machine Learning: Beyond Information Modeling to Insights Discovery
3. Adapting Machine Learning Algorithms to Novel Applications: 8-10 Examples

Background Knowledge
- Machine Learning Algorithms;
- Basic Mathematics and Statistics.


Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA's Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

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