Unsupervised Learning | Foundations for Energy Data Analytics скачать в хорошем качестве

Unsupervised Learning | Foundations for Energy Data Analytics

Dive into this foundational video on #unsupervisedlearning by Dr. Jordan Malof (Duke University). This talk introduces common unsupervised learning techniques and how they can be applied to energy challenges. You’ll hear about #mixedclustering, #dimensionalityreduction, and more! 0:00 – Intro / supervised learning 1:49 – Unsupervised learning overview 4:36 – Gaussian mixture clustering 9:33 – Common unsupervised learning tasks 9:58 – Dimensionality reduction 13:31 – Generative modeling 16:49 – Conclusions Part of the Foundations for Energy Data Analytics Series. Some videos in the series (like this one!) introduce data science concepts and techniques that are applicable to energy challenges. Others are designed to provide background on energy systems and policy, elucidating contexts that are rich with opportunities for data scientists. (   • Foundations for Energy Data Analytics  ) This talk was originally presented during a workshop of the Energy Data Analytics Ph.D. Student Fellows Program (https://energy.duke.edu/energy-data-a..., organized by the Energy Data Analytics Lab (https://energy.duke.edu/research/ener...) at Duke University. The Fellows Program is funded by a grant from the Alfred P. Sloan Foundation, Grant-G2020-13922. (https://sloan.org) (Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff). Follow the Duke University Energy Initiative: Email list - https://bit.ly/energyduke LinkedIn -   / duke-university-energy-initiative   Twitter -   / dukeuenergy   The Energy Initiative is merging with Duke’s Nicholas Institute for Environmental Policy Solutions. Learn more: http://bit.ly/ni-ei