Scaling Up Bayesian Inference for Big and Complex Data

This course focuses on scaling up Bayesian inference for big and complex data. The learning outcomes include understanding computational challenges in machine learning, exploring mixing rate problems, playing MCMC games, addressing big N problems, utilizing stochastic approximation, solving sparse linear programs, and discussing theoretical guarantees. The course teaches skills such as implementing logistic regression, Gaussian process models, and popular algorithms. The teaching method involves lectures and theoretical discussions. The intended audience for this course includes data scientists, machine learning engineers, statisticians, and researchers interested in Bayesian inference and large-scale data analysis.

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