KAIST MAS110 · Linear Algebra for Data Science · Fall 2026
Practice Sessions
Connect lecture ideas to readable code and visible mathematical change.
Review the idea, trace the code, then run the full notebook in Colab. Based on Foundations of LADS; not a homework-solution bank.
Practice library
Browse by chapter
Available chapters open below. The rest are marked in preparation.
- 02Matrices and Gaussian EliminationMatrix systems, elimination, block matrices, LU decomposition, and graph matrices.4 notebooksAvailable now
- 03Vector Spaces and TransformationsSubspaces, null and column spaces, rank, bases, and linear transformations.3 notebooksAvailable now
- 04Orthogonality and ApproximationInner products, projections, Gram-Schmidt, QR, matrix norms, and least squares.4 notebooksIn preparation
- 05Singular Value DecompositionSingular directions, low-rank approximation, pseudoinverses, and numerical stability.4 notebooksIn preparation
- 06SVD in PracticeLow-dimensional structure in images, MNIST digits, and financial time series.3 notebooksIn preparation
- 07Positive Definite MatricesQuadratic forms, Cholesky factorization, matrix square roots, and geometry.2 notebooksIn preparation
- 08DeterminantsDeterminants, geometric scaling, and matrix update identities.1 notebookIn preparation
- 09Eigenvalues and DiagonalizationEigenpairs, similarity, change of basis, and spectral decomposition.1 notebookIn preparation
- 10Perron-Frobenius and PageRankPositive eigenvectors, power iteration, PageRank, and eigenvalue adjustment.2 notebooksIn preparation
- 11Jordan FormCanonical matrix structure when diagonalization alone is not enough.1 notebookIn preparation
In each lab
Review, predict, compare
- 01Key ideas
Review the terms used in the lab.
- 02Line mechanics
Read the action, shape, and operation.
- 03Predict
Decide what should change.
- 04Reveal
Compare the prepared before and after.