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Bayesian Methods and Probabilistic Models with Allen Downey
Tools like PyMC and Stan make it easy to implement probabilistic models, but getting started can be challenging. In this talk I present a strategy for simultaneously developing and implementing probabilistic models by alternating between forward and inverse probabilities and between grid algorithms and MCMC. This process helps developers validate modeling decisions and verify their implementation. As an example, I will use a version of the "Boston Bruins problem," which I presented in Think Bayes, updated for the 2017-18 season. I will also present and request comments on my plans for the second edition of Think Bayes.
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Talks from some of the leading visionaries and bleeding-edge researchers in AI/ML: Fei-Fei Li on visual intelligence in computers and ImageNet; Eric Horvitz on AI solutions in the open world; and Tom Mitchell on using ML to study how the brain creates and represents language.
Register now for the next ACM Learning Webinar, "The Future of Wireless and What It Will Enable," presented live on Wednesday, April 3, by Andrea Goldsmith, Stephen Harris Professor in the School of Engineering at Stanford University and 2018-2019 ACM Athena Lecturer. Continue the discussion and checkout further resources on ACM's Discourse Page.
View the most recent ACM Learning Webinar, "Building a Culture to Support Inclusive Design," on demand. The talk was presented by Jen Devins, Google Accessibility UX Lead and Nithya Sambasivan, Senior User Experience Researcher at Google. Continue the discussion and checkout further resources on ACM's Discourse Page.