King Abdullah University of Science and Technology - Fall 2019
This course is a mathematically rigorous introduction to the emerging field of big data optimization, focused on algorithms suitable for training supervised machine learning models. However, the methods and principles covered have countless applications in other fields, including data science, signal processing, engineering, and industry. The focus is on algorithms and associated theory. Randomized/stochastic algorithms play a dominant role. The course is based on a novel and unified approach to recent developments in the field developed by the lecturer.
Big data optimization is the study of optimization problems described by big quantities of data, where "big" is loosely defined as large enough for traditional approaches to suffer or not be applicable at all. As we live in a digital age where it is increasingly easier to collect and store data in digital form (e.g., transaction records, YouTube clicks, internet activity, Wikipedia, twitter, customer behaviour databases, government records, image collections), big data problems are becoming ubiquitous. New methods and tools are needed to analyze such vast datasets, and optimization algorithms are at the heart of such efforts, underpinning much of data science, including machine learning, operations research and statistical analysis. Alongside computer science and statistics, optimization is one of the pillars of big data analysis.
The course will cover topics such as supervised learning, empirical risk minimization, big data problems, stochastic gradient descent, mini-batching, importance sampling, arbitrary sampling, variance reduction, quantization and compression for distributed training, federated learning, convex feasibility problems, high dimensional problems, randomized coordinate descent, and acceleration.
(The course will contain a substantial amount of new material this year compared to previous two years)
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