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Multivariate Methods I
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
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Explores orthogonality, vector norms, and subspaces in Euclidean space, including determining orthogonal complements and properties of subspaces and matrices.
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Explores norm, dot product, and orthogonality in vector spaces, including properties and inequalities.
Orthogonality and Least Squares
Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.
Linear Algebra: Normal Equations and Symmetric Matrices
Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.
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Covers the error analysis for the Galerkin scheme and the importance of Galerkin orthogonality.
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