This course introduces students to the principles of engineering computations and probability/statistics. Its purpose is also to develop the student's ability to write MATLAB code to solve numerical and statistical problems of engineering interest.
Availability2017 Course Timetables
- Semester 2 - 2017
- Semester 2 - 2018
- Trimester 3 - 2018 (Singapore)
On successful completion of the course students will be able to:
1. Construct basic numerical algorithms in MATLAB for computing solutions of single nonlinear equations, systems of linear equations and to manage basic linear algebra.
2. Implement basic algorithms for interpolation, curve fitting, numerical differentiation, and numerical integration with MATLAB.
3. Implement basic numerical methods for solving parabolic and hyperbolic partial differential equations and processing optimization with MATLAB.
4. Demonstrate the key concepts of probability, basic Bayesian inference and also with the Monte Carlo method for both integration and for solving basic probability problems.
5. Formulate and solve basic problems dealing with probability and statistics in basic mechanical engineering applications.
The content of the course includes:
- The numerical solution of a single nonlinear equation, systems of linear equations, ordinary differential equations, and partial differential equations. Optimization.
- The numerical implementation of interpolation and curve fitting.
- An introduction to probability and distribution theory targeted mainly to mechanical engineering applications.
- Monte Carlos simulation: basic theory and practice.
- Introduction to Bayesian statistical inference with applications to common mechanical engineering probability models.
GENG1003 and MATH2310
Written Assignment: Assignment 1
Written Assignment: Assignment 2
Quiz: Quiz 1
Quiz: Quiz 2
Quiz: Quiz 3
Tutorial / Laboratory Exercises: Computer Lab Exercises
Callaghan and UoN Singapore
Face to Face On Campus 2 hour(s) per Week for Full Term
Face to Face On Campus 3 hour(s) per Week for Full Term
Face to Face On Campus 1 hour(s) per Week for Full Term