Gaussian processes in machine learning

Prerequisite

Probability theory, Mathematical statistics, Machine learning

Abstract

Machine learning considers many models. Some are interpretable, others are probabilistic, and others are used in practice. Gaussian process-based models have all these properties: they are interpretable, probabilistic, and lead to practical solutions. The history of applications of Gaussian process regression in machine learning lasts for more than 60 years, while some recent works revolutionized the scope of practical applications of it and theoretical tools to answer fundamental questions. Applications include helping train the models with superhuman powers in the Go game and reducing the cost of aircraft construction by 10%, saving millions of dollars in both cases. I plan to achieve two main goals in this course: provide an interesting researcher with a powerful tool named Gaussian process-based machine learning models and equip him with a theoretical understanding of the success of Gaussian process regression. In particular, we'll find out how this approach solves the regression problem interpretably, how to estimate the uncertainty in a principled way, and how to select the optimal design of experiments. The lectures are based on my 10 years of experience in this field.

Lecturer Intro.

Alexey has deep expertise in machine learning and processing of sequential data. He publishes at top venues, including KDD, ACM Multimedia and AISTATS. Industrial applications of his results are now in service at companies Airbus, Porsche and Saudi Aramco among others.