The UT SNR ‘Data Science Institute for Machine Learning and AI (DSIMLA)’ is a virtual Institute where faculty, staff, and students from The University of Tennessee will support the mission and accomplish the objectives of the Institute under the direction of a Director and Executive Board. Given that machine learning technologies and software capabilities are the core element of AI and are advancing at almost an exponential rate (e.g., random forests, boosted trees, Bayesian additive regression trees – BART, multivariate adaptive regressions splines – MARS, etc.), the ‘ML Coop’ will operate as an autonomous group within the Institute. The ML Coop will consist of a broad group of academics and industry representatives. The ML Coop will conduct highly specialized webinars, trainings, and research projects with sole purpose of ML application in manufacturing and forestry.
The education component of the UT SNR DSMILA establishes a curriculum for students and industry focused on the general aspects of data analytics, which will be a combination of well-established statistical principles and contemporary methods for continuous improvement. The academic curriculum is structured to support the MS Forest Business program within the UT SNR. The MS Forest Business program is open to students at UT and industry personnel willing to expand their education while working full-time, and will consist of a mostly virtual curriculum. The industry curriculum for live webinars, workshops, and customized training is heavily influenced from the guidance of the executive board.
The MS in Forest Business is a three-semester, non-thesis program that prepares students to assume leadership roles within forest industry in three separate tracks: Analytics & Data Science, Forestland Investment & Finance, and Logistics & Procurement. The program consists of a core of classes focused on accounting and finance principles, as well as an internship and final project. Students who are currently employed in the industry can use their current work experience for the internship and project requirements. Beyond the core, students select from a suite of course within the three tracks.
The research component focuses on applying data science in forest products manufacturing and forestry. TQdM will be part of this research. The ML Coop supports research with successful applications of machine learning algorithms and AI. This research also consists of programming languages for successful applications, e.g., Python, R, SAS-JMP, Minitab, etc. Data fusion algorithms and code (e.g., SQL) will be part of the research of the ML Coop.