Statistical Learning and Sequential Prediction (Alexander Rakhlin, et al)

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Statistical Learning and Sequential Prediction (Alexander Rakhlin, et al)

The theoretical features of statistical learning and sequential prediction are the main subjects of this work. These two topics have previously received distinct treatment within the academic community.

It uses a consistent methodology to examine learning in both situations. It combines concepts from probability and statistics, game theory, algorithms, and optimization to achieve this. The authors seek to portray the energy that makes this combination of thoughts makes the topic exciting to us.

The authors will make every effort to keep the course as self-contained as they can, and whenever further readings are required, links to them will be supplied. Graduate students with a strong foundation in probability and linear algebra make up the target audience.

What makes machine learning important? Many of the jobs we want computers to do can't be hard-coded. Programs need to be adjusted. The objective is to then encode as much domain-specific information as is required for a given application while yet allowing the system enough latitude to enhance observed data.

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