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Welcome to AI matters 5(2)., and . AI Matters, 5 (2): 3 (2019)Using spatiotemporal relational random forests to improve our understanding of severe weather processes., , , , , and . Stat. Anal. Data Min., 4 (4): 407-429 (2011)A Summary of the Twenty-Ninth AAAI Conference on Artificial Intelligence., , , , , , , , , and 9 other author(s). AI Mag., 36 (3): 99-106 (2015)ACM SIGAI activity report., , , , , , , , , and 7 other author(s). AI Matters, 5 (3): 6-11 (2019)Creating significant learning experiences in introductory artificial intelligence., and . SIGCSE, page 39-43. ACM, (2007)A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning., , , , and . CoRR, (2022)Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning., , , , and . Mach. Learn., 95 (1): 27-50 (2014)AI profiles: an interview with Maja Matarić., and . AI Matters, 3 (3): 12-14 (2017)Steerable Clustering for Visual Analysis of Ecosystems., , , and . EuroVA@EuroVis, Eurographics Association, (2011)Spatiotemporal Relational Probability Trees: An Introduction., , , , and . ICDM, page 935-940. IEEE Computer Society, (2008)