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References

This page provides references for classic papers and books in various field of machine learning, big data analysis. For the beginner and new students, it provides foundations for your own research. For senior students, we expect you to get inspired by previous work of other pioneers in your field.

Machine Learning

General Surveys

Spectral Methods

Dimensionality Estimation

Online learning and Boosting

Sparse coding, dictionary learning and matrix factorization

Deep learning, neural network, feature learning

Learning from multiple sources

Random Geometric Graphs and Networks

Differential Geometry in statistics, information theory and learning

Information Divergence Estimation and Applications

Target Detection/Tracking/Localization

Adaptive Sensing

LaTeX tools

* TIKZ and PGF for drawing within LaTeX slides

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