Machine Learning Platforms, Technology, and Tools
The course discusses the computational cyberinfrastructure that is necessary for enabling machine learning with big data. It explores big data lakes and data warehouses, discussing these two alternative enterprise repositories, and their relative strengths and drawbacks. Computational systems that facilitate machine learning in the enterprise are reviewed, as well as the concepts and techniques necessary for deploying scalable machine learning into business processes.
Grant Scott is an Assistant Professor in the Center for Geospatial Intelligence (CGI) and the Electrical Engineering and Computer Science Department at the University of Missouri.
Throughout his career, Dr. Grant Scott has conducted extensive research on scaling machine learning up for big data. His research focuses on Applied Machine Learning, Computer Vision, as well as Advanced Pattern Analysis, High-dimensional Data Analytics, Advanced Data Systems, and Multi-modal Analytics.
Dr. Scott is a Senior Member of the IEEE Computational Intelligence Society and the IEEE Geoscience Remote Sensing Society.
Publication Year: 2020