CS 357 Textbook
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Notes
  • 2. Python
  • 3. Errors and Complexity
  • 4. Floating Point Representation
  • 5. Rounding
  • 6. Taylor Series
  • 7. Random Number Generators and Monte Carlo Method
  • 8. Vectors, Matrices, and Norms
  • 9. LU Decomposition for Solving Linear Equations
  • 10. Sparse Matrices
  • 11. Condition Numbers
  • 12. Eigenvalues and Eigenvectors
  • 13. Markov chains
  • 14. Finite Difference Methods
  • 15. Solving Nonlinear Equations
  • 16. Optimization
  • 17. Least Squares Fitting
  • 18. Singular Value Decomposition (SVD)
  • 19. Principal Component Analysis (PCA)
Slides
CS 357 Textbook
  • slides
  • README.md

Slides

Here are all the slides from each topic in the class.

  • 02-Python.pdf

  • 03-Errors.pdf

  • 04a-Binary-Numbers.pdf

  • 05-Rounding.pdf

  • 06-Taylor.pdf

  • 07-MonteCarlo.pdf

  • 08-Vectors-and-Matrices.pdf

  • 09-Linear-Systems.pdf

  • 10-Conditioning.pdf

  • 11-Sparse-Matrices.pdf

  • 12-Eigenvalues.pdf

  • 13-Markov-Chains.pdf

  • 14-NonLinear-Equations.pdf

  • 15-Optimization.pdf

  • 16-SVD.pdf

  • 17-LinearLeastSquares.pdf

  • 18-PCA.pdf

  • 19-Finite-Difference.pdf

  • 94-Floating-Point.pdf



2025, CS 357 @ UIUC Course Staff
CS 357 Textbook
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