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Preface Preliminaries: before we begin 1. Matrices and vectors 2.

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Systems of linear equations 3. Matrix inversion and determinants 4.

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Rank, range and linear equations 5. Vector spaces 6.

Linear independence, bases and dimension 7. Linear transformations and change of basis 8. Diagonalisation 9.

10 Examples of Linear Algebra in Machine Learning

Applications of diagonalisation Inner products and orthogonality Orthogonal diagonalisation and its applications Direct sums and projections Complex matrices and vector spaces Comments on exercises Index. Skip to main content. Search form Search.

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Student connections of linear algebra concepts: an analysis of concept maps

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    Basics of Linear Algebra - Engineering Mathematics

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