Linear algebra for CS
Undergraduate · CS / Programming
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Core linear algebra
- Vectors in R^n; dot product, norms, and angles
- Matrices, matrix multiplication, and linear maps
- Systems of equations; Gaussian elimination
- Rank, null space, and column space
- Determinants and invertibility (computational view)
Eigenmethods
- Eigenvalues and eigenvectors
- Diagonalization and spectral theorem (symmetric case)
- Orthogonality, projections, and Gram–Schmidt
- Least squares and normal equations
- Singular value decomposition (intro)
STEM / applied
CS applications
- Transformations for computer graphics
- PageRank as eigenvector problem (intro)
- PCA for dimensionality reduction
- Solving linear systems in ML (normal equations, regularization)
- Numerical stability and conditioning (intro)
Computation
- Implementing matrix ops in NumPy/Python
- Sparse matrices for graphs and networks (intro)
- Iterative methods: power iteration, conjugate gradient (survey)
- GPU matrix multiply overview (intro)
- Using LA libraries vs rolling your own
Notes
Often cross-listed with math departments; CS sections emphasize applications over abstract proofs.