This course provides an introduction of mathematical data science, including high-dimensional data analysis and basic machine learning algorithms. Topics include singular value decomposition, low-rank approximation, principal component analysis, k-means, spectral clustering, topic models, nonnegative matrix factorization, together with various applications such as compressive sensing, image recovery and natural language processing.

This course provides an introduction of mathematical data science, including high-dimensional data analysis and basic machine learning algorithms. Topics include singular value decomposition, low-rank approximation, principal component analysis, k-means, spectral clustering, topic models, nonnegative matrix factorization, together with various applications such as compressive sensing, image recovery and natural language processing.