Unsupervised Learning: From Big Data to Low-Dimensional Representations

Johns Hopkins University Course , Fall 2017 , Prof. René Vidal

413 students enrolled

Overview

In the era of data deluge, the development of methods for discovering structure in high-dimensional data is becoming increasingly important. This course will cover state-of-the-art methods from algebraic geometry, sparse and low-rank representations, and statistical learning for modeling and clustering high-dimensional data. The first part of the course will cover methods for modeling data with a single low-dimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and low-rank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging.

Lecture 1: Lecture 1 | Syllabus + Introduction + Basics of Linear Algebra (Hopkins)

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        Lecture Details

        Unsupervised Learning: Spring 2017 at Johns Hopkins University. Professor Rene Vidal.

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