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EECS 208

Computational Principles for High-dimensional Data Analysis

4 credits · Graduate

Introduction to fundamental geometric and statistical concepts and principles of low-dimensional models for high-dimensional signal and data analysis, spanning basic theory, efficient algorithms, and diverse real-world applications. Systematic study of both sampling complexity and computational complexity for sparse, low-rank, and low-dimensional models – including important cases such as matrix completion, robust principal component analysis, dictionary learning, and deep networks.

Prerequisites & course information

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Sources