A Primer on Machine Learning(ML)/Artificial Intelligence (AI), concepts, and terminology
About this presentation
Machine Learning and Artificial Intelligence (ML/AI) have been used since the 1950s when Arthur Samuel of IBM created a computer program for playing checkers. Since then, ML/AI applications have evolved a good bit. Still, there are some base structures and milestone events that form the basis of good ML/AI applications and their software.
Falk Huettmann, Ph.D., MBA, Professor - EWHALE Lab- Institute of Arctic Biology, Biology & Wildlife Department, University of Alaska Fairbanks, will present in lay terms for a general audience the basic terminology, key concepts, and algorithms for how a cloud of points can be analyzed with ML/AI; an emphasize is made for uses in healthcare.
This presentation will cover linear and non-linear applications, regression and classification trees, boosting and bagging (bootstrap aggregation; Random Forest), maximum entropy, Vector Support Machines, Neural Networks, Ensemble Models, and wider 'The Cloud' workflow applications with MS Azure and ORACLE. Dr. Huettmann will address research design, significances, spatial and temporal applications, model selection, multivariate predictors, data mining, and 'inference from predictions' (Leo Breiman) as well as why 'many weak learners make for a strong learner' (J. Friedman). Some software examples in commercial tools, R and python are briefly mentioned emphasizing the need for transparent and repeatable research and associated open-access data.