New course notes detail advanced numerical linear algebra for ML and PDEs

This paper, "Advanced Linear Algebra with Applications - Part I," presents a master's-level course on numerical linear algebra, focusing on its growing importance beyond traditional partial differential equations. It highlights how large-scale problems in machine learning, data assimilation, and network analysis share common numerical linear algebra challenges. The notes cover essential topics like norms, factorizations, iterative methods (conjugate gradient, Lanczos, Arnoldi, GMRES), and preconditioning techniques, all illustrated with Python code. AI

IMPACT Provides foundational mathematical tools and algorithms crucial for developing and understanding advanced machine learning models.

RANK_REASON The item is a research paper (lecture notes) detailing advanced mathematical concepts and algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

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• Arnoldi
• arXiv
• conjugate gradient method
• Data assimilation
• Generalized minimal residual method
• Lanczos methods
• machine learning
• multigrid method
• numerical linear algebra
• PageRank
• partial differential equations
• Python
• spectral clustering

• paper
• other

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