This event will be Livestreamed:|
Whats going to happen, & when? Those are the million dollar questions were all trying to answer with data science. But beneath the glitz & glamour of machine learning models are hundreds of years of work in statistics & probability. So to be successful at machine learning, at data science, requires at least a familiarity with the foundational methods & concepts.
In this one-hour lecture well take a tour of the statistics & probability underlying many machine learning models, including defining some theorems. Our path will be meandering & is not intended to be comprehensive, though it should give you a flavor of the statistics required for data science. Well meet Gauss, Laplace, Bernoulli & Fisher. Well visit Italy, England, & France. No specific background beyond high-school mathematics will be assumed.
Lara is constantly awe struck by how math & statistical models can so reasonably approximate the messy real world, & loves to share her passion for the theory.
Lara comes to Metis from McKinsey, where she worked with financial institutions on risk modeling. Prior to settling into the practical world of consulting, Lara received a master's from the University of Chicago, where she had the privilege of thinking about problems with no real-world implications. There her passion for unstructured thinking & novel problem solving were nurtured & she took the lessons learned with her into conference rooms & PowerPoint decks. The statistical & mathematical elegance of data science are what animate her the most, but she gets her biggest joy from demystifying the rigorous mathematics for all audiences. If forced to snap out of her mathematical reveries, she's probably doing yoga, riding a bike, or drinking too much coffee.
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