Lesson 1: Linear Algebra and Matrix Computation in MATLAB
Lesson Overview
Develop practical capability in linear algebra and matrix computation in matlab as part of the Essential Mathematics and Statistics for Machine Learning with MATLAB learning path.
Topics Covered
Vectors, matrices, shapes, indexing, and broadcasting concepts
Matrix multiplication and systems of linear equations
Rank, determinant, inverse, eigenvalues, and eigenvectors
Norms, distance, similarity, and projections
Singular value decomposition concepts
Applications to feature spaces and dimensionality reduction
Practical Activity
Build a guided MATLAB exercise covering vectors, matrices, shapes, indexing, and broadcasting concepts.
Apply matrix multiplication and systems of linear equations to a practical engineering or data scenario.
Validate results and troubleshoot an issue involving applications to feature spaces and dimensionality reduction.
Key Takeaways
Explain and apply vectors, matrices, shapes, indexing, and broadcasting concepts.
Use rank, determinant, inverse, eigenvalues, and eigenvectors in a structured workflow.
Validate outputs with plots, metrics, tests, or simulation evidence.
