Alexander Novikov

Alexander Novikov graduated Moscow State University and is now a research scientist at International lab of deep learning and Bayesian methods and at the same time a Ph.D. student in Institute of Numerical Mathematics. His research focuses on machine learning spanning Bayesian methods, tensor decompositions, deep learning, and reinforcement learning. Alexander published papers at major machine learning conferences and also got industrial experience consulting Huawei, interning at Google search infrastructure team in USA headquarters and at DeepMind under the supervision of Nando de Freitas.

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Bayesian Methods for Machine Learning (Coursera)

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like [...]
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