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UC Berkeley

Fall 2013

This course investigates the mathematical principles behind data and information analysis. It brings together concepts from statistics, optimization, and computer science, with a focus on large deviation inequalities, and convex analysis. It's tailored towards advanced graduate students who wish to incorporate these theories into their research.

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+ 20 more conceptsPrinceton University

Fall 2017

A thorough introduction to machine learning principles such as online learning, decision making, gradient-based learning, and empirical risk minimization. It also explores regression, classification, dimensionality reduction, ensemble methods, neural networks, and deep learning. The course material is self-contained and based on freely available resources.

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