Enhancing Machine Learning with Bayesian Probability: Quantum Control & Cookie Recipes
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In this riveting Computerphile episode, the team delves into the world of machine learning bolstered by the formidable Bayesian probability Theory. They unveil how Bayesian methods inject a dose of confidence into predictions through what they call "sausage plots," painting a vivid picture of uncertainty levels between data points. This isn't just any run-of-the-mill machine learning talk; it's about enhancing predictions with a dash of swagger and certainty, a concept that sets Bayesian apart from the rest.
But wait, there's more! The resurgence of Bayesian principles in the 20th century, thanks to its pivotal role in cracking the Enigma code and identifying German submarines during World War II, is a tale of triumph against all odds. Fast forward to today, where Bayesian shines bright in the realm of deep learning, offering not just answers but distributions of confidence levels, tackling the very essence of reliability and robustness in machine learning. It's like having a trusty co-pilot guiding you through the treacherous waters of uncertainty.
The speaker's research takes us on a thrilling ride into the world of Bayesian optimization, a high-stakes game of balancing exploitation and exploration. Picture this: you're on a quest to find the lowest function value, navigating through uncharted territories of uncertainty while chasing down those elusive peaks of success. It's a strategic dance of risk and reward, all orchestrated by the principled mathematics of Bayesian decision Theory. From controlling Quantum devices to optimizing cookie recipes, Bayesian optimization emerges as the unsung hero in the grand opera of Science and Engineering, revolutionizing the way we make decisions and extract value from every experiment.
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Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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