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Curriculum
- 1 Section
- 38 Lessons
- 10 Weeks
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- Numerical Analysis38
- 2.1Lecture 1: Programing Basics
- 2.2Lecture 2: Introduction to Pointers
- 2.3Lecture 3: Pointers And Arrays
- 2.4Lecture 4: External Functions and Argument Passing
- 2.5Lecture 5: Representation of Numbers
- 2.6Lecture 6: Numerical Error
- 2.7Lecture 7: Error Propagation and Stability
- 2.8Lecture 8: Polynomial Interpolation I
- 2.9Lecture 9: Polynomial Interpolation II
- 2.10Lecture 10: Error In Interpolation Polynomial
- 2.11Lecture 11: Polynomial Interpolation
- 2.12Lecture 12: Cubic Spline Interpolation
- 2.13Lecture 13: Data Fitting Linear Fit I
- 2.14Lecture 14: Data Fitting Linear Fit II
- 2.15Lecture 15: Data Fitting Non Linear Fit
- 2.16Lecture 16: Matrix Elimation and Solution
- 2.17Lecture 17: Solution To Linear Equations
- 2.18Lecture 18: Matrix Elimination
- 2.19Lecture 19: Eigen Values of A Matrix
- 2.20Lecture 20: Eigen Values And Eigen Vectors
- 2.21Lecture 21: Solving NonLinear Equations
- 2.22Lecture 22: Solving NonLinear Equations Newton Rapson Method
- 2.23Lecture 23: Methods For Solving NonLinear Equations
- 2.24Lecture 24: System of NonLinear Equations
- 2.25Lecture 25: Numerical Derivations
- 2.26Lecture 26: High order Derivatives From Difference Formula
- 2.27Lecture 27: Numerical Integration Basic Rules
- 2.28Lecture 28: Comparison of Different Basic Rules
- 2.29Lecture 29: Gaussian Rules
- 2.30Lecture 30: Comparison of Gaussian Rules
- 2.31Lecture 31: Solving Ordinary Differential Equations I
- 2.32Lecture 32: Solving ordinary differential equations II
- 2.33Lecture 33: Adaptive step size Runge Kutta scheme
- 2.34Lecture 34: Partial Differential Equations
- 2.35Lecture 35: Explicit and Implicit Methods
- 2.36Lecture 36: The Crank Nicholson Scheme For Two Spatial
- 2.37Lecture 37: Fourier Transforms
- 2.38Lecture 38: Fast Fourier Transforms