|
| 1.
| [Apply]
computational arithmetic in real-world scenarios: students can handle floating point numbers, rounding errors, relative and absolute errors, cancellation, and numerical instability |
| 2.
| [Solve]
linear and non-linear equation systems, using Gaussian elimination, LU, QR and Cholesky decomposition, pivoting, norms, condition numbers iterative methods, and the bisection method, Newton’s method, the secant method respectively |
| 3.
| [Recognise]
and apply various optimization techniques, such as the Golden section search, the steepest descent method, and the Newton method |
| 4.
| [Interpolate]
data; students will learn data interpolation for approximation through the application of Lagrange interpolation, Newton interpolation, Hermite interpolation, and piecewise polynomial interpolation |
| 5.
| [Integrate]
and differentiate numerical functions using forward difference approximation, Richardson extrapolation, the composite rule, Newton-Cotes quadrature, and Gaussian quadrature |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| | PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
| CLO 1 | T,P | T,P | T,P | | | | | | | |
| CLO 2 | T,P | T,P | T,P | T,P | | | | | | |
| CLO 3 | T,P | T,P | T,P | | | | T | | | |
| CLO 4 | T,P | T,P | T,P | | | | T | | | |
| CLO 5 | T,P | T,P | T,P | | | | T | | | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
|
|
Calendar Entry:
This course provides an overview and covers the fundamentals of scientific and numerical computing. Topics include numerical analysis and computation, symbolic computation, scientific visualization, architectures for scientific computing, and applications of scientific computing.
|
|
Detailed Description:
| Computer Arithmetic |
Mapped to CLOs
|
| floating point numbers, rounding errors, relative and absolute errors, cancellation, and numerical instability. | 1 |
| Solving linear system of equation and optimization |
Mapped to CLOs
|
| Solve linear and non-linear equation systems, using Gaussian elimination, LU, QR and Cholesky decomposition, pivoting, norms, condition numbers iterative methods, and the bisection method, Newton’s method, the secant method respectively | 2 |
| Optimization techniques, such as the Golden section search, the steepest descent method, and the Newton method | 3 |
| Data interpolation and fitting |
Mapped to CLOs
|
| Interpolate data; students will be proficient in data interpolation for approximation through the application of Lagrange interpolation, Newton interpolation, Hermite interpolation, and piecewise polynomial interpolation | 4 |
| Least squares data fitting, Normal equation, QR decomposition | 4 |
| Integration and differentiation |
Mapped to CLOs
|
| Integrate and differentiate numerical functions using forward difference approximation, Richardson extrapolation, the composite rule, Newton-Cotes quadrature, and Gaussian quadrature. | 5 |
|
