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)
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