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dc.contributor.authorKim, Nathan
dc.contributor.authorHan, David
dc.date.accessioned2021-02-06T23:02:11Z
dc.date.available2021-02-06T23:02:11Z
dc.date.issued2020-12
dc.identifier.issn2470-3958
dc.identifier.urihttps://hdl.handle.net/20.500.12588/245
dc.description.abstractThe use of Artificial Intelligence, machine learning and deep learning have gained a lot of attention and become increasingly popular in many areas of application. Historically machine learning and theory had strong connections to statistics; however, the current deep learning context is mostly in computer science perspectives and lacks statistical perspectives. In this work, we address this research gap and discuss how to teach deep learning to the next generation of statisticians. We first describe some backgrounds and how to get motivated. We discuss different terminologies in computer science and statistics, and how deep learning procedures work without getting into mathematics. In response to a question regarding what to teach, we address organizing deep learning contents and focus on the statistician’s view; form basic statistical understandings of the neural networks to the latest hot topics on uncertainty quantifications for prediction of deep learning, which has been studied in the Bayesian frameworks. Further, we discuss how to choose computational environments and help develop programming skills for the students. We also discuss how to develop homework incorporating the idea of experimental design. Finally, we discuss how to expose students to the domain knowledge and help to build multi-discipline collaborations.en_US
dc.language.isoen_USen_US
dc.publisherUTSA Office of Undergraduate Researchen_US
dc.relation.ispartofseriesThe UTSA Journal of Undergraduate Research and Scholarly Work;
dc.subjectundergraduate student worksen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectcurriculum developmenten_US
dc.subjectmachine learningen_US
dc.subjectneural networken_US
dc.subjectstatistical educationen_US
dc.titleStatistical Perspectives in Teaching Deep Learning from Fundamentals to Applicationsen_US
dc.typePosteren_US
dc.description.departmentManagement Science and Statistics


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