STEM / applied
Linear algebra for CS · Undergraduate · CS / Programming
Topics
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
Computation and ML bridges
- Implementing matrix ops efficiently (BLAS intuition)
- Sparse matrices in search and recommender systems
- PCA pipeline on a small dataset
- Linear algebra behind neural net layers (survey)
- Solving Ax=b in libraries (NumPy/SciPy/Eigen)
- Capstone: apply SVD/PCA or least squares to a CS dataset
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$1,162 · Linear algebra for CS · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.