Part A: Course Overview

Course Title: Numerical Techniques

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2391

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2022,
Sem 2 2023,
Sem 1 2024,
Sem 1 2025

Course Coordinator: Dr. Mahshid Sadeghpour

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: mahshid.sadeghpour@rmit.edu.au

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study 

You should have satisfactorily completed or received credit for the following courses before you commence this course: 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer. 

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning. 

Please follow the link for further information on how to apply for credit for prior study or experience. 

Assumed Knowledge

Basic knowledge in manipulating algorithms and programming with Maple, Mathematica or Matlab


Course Description

Numerical Techniques introduces and studies fundamental operations and methods that are the tools of mathematicians, statisticians and applied scientists. The course introduces the numerical methods necessary for the determination of the errors in computation, the manipulation of large data, numerical linear algebra, solution of linear and nonlinear equations, systems of ordinary differential equations, the evaluation of definite integrals by numerical quadrature and the approximation of functions and data. The foundation is laid for the more specialist mathematics courses that are undertaken in subsequent years. This course provides the basic computational skills required for all courses in mathematics, computer science, applied sciences and engineering.


Objectives/Learning Outcomes/Capability Development

This course contributes to the program learning outcomes for the following programs: 

BH101AMS - Bachelor of Science (Dean's Scholar, Applied Mathematics and Statistics) (Honours)  

PLO 2 Knowledge and Technical Competence 
PLO 3 Problem Solving 
PLO 5 Communication 

BP083P20 - Bachelor of Science (Applied Mathematics and Statistics) 

PLO2 Knowledge and Technical Competence 
PLO5 Communication  

BP350 - Bachelor of Science Mathematics  

PLO 1 Apply a broad and coherent knowledge of scientific theories, principles, concepts and practice in one or more scientific disciplines.  
PLO 2 Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities. 
PLO 3 Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making. 
PLO 4 Communicate, report and reflect on scientific findings, to diverse audiences utilising a variety of formats employing integrity and culturally safe practices. 

For more information on the program learning outcomes for your program, please see the program guide. 


On successful completion of this course, you should be able to:

  1. Solve nonlinear equations using various numerical methods such as bisection method, Newton’s method, secant method and fixed-point iteration method and implement using a computer.
  2. Solve large systems of linear equations using Gaussian elimination, factorisation methods, implement using a computer and identify where numerical error may occur.
  3. Approximate functions and data using polynomial and rational interpolation or polynomial and rational least squares approximation and explain the concept of error estimation.
  4. Solve a system of ordinary differential equations using various numerical methods (taking into account criteria such as convergence, rate of convergence, accuracy and, where appropriate, consistency and stability) and implement using a computer. 
  5. Evaluate definite integrals using numerical quadrature (such as Gaussian quadrature, Newton-Cotes methods) and implement using a computer.
  6. Numerically determine eigenvalues and eigenvectors for very large matrices using a variety of methods.


Overview of Learning Activities

This course is presented using a mixture of classroom instruction; problem-based practical classes; exercises; timed assessments and programming assignments. 

You will be provided with the opportunity to clarify main concepts through questions that are designed to promote teamwork and critical thinking. You will be encouraged to work in small groups, but to present your own solution to each set task. This will be achieved through practice class sessions where you will receive feedback whilst attempting to formulate mathematical models and to determine their solutions. It will provide a forum for you to discuss your solution strategies with colleagues and in these discussions to develop your analytical ability and communication skills. Staff members will oversee these activities responding when necessary. 

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course. 


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks

Assessment Task 1: Problem-solving exercises 
Weighting 45% 
This assessment supports CLOs 1, 2, 3 & 4 

Assessment Task 2: Programming Assignments 
Weighting 40% 
This assessment supports CLOs 1, 2, 3, 4 & 5 

Assessment Task 3: In-class Practical Test 
Weighting 15% 
This assessment supports CLOs 2, 3, 4, 5 & 6 

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.