Course Title: Applied Linear Algebra

Part A: Course Overview

Course Title: Applied Linear Algebra

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2016


City Campus


171H School of Science


Sem 2 2017,
Sem 2 2018,
Sem 2 2019

Course Coordinator: Dr Graham Clarke

Course Coordinator Phone: +61 3 9925 3225

Course Coordinator Email:

Course Coordinator Location: 8.9.62

Pre-requisite Courses and Assumed Knowledge and Capabilities

Math 1150 Discrete Mathematics.

Course Description

This course gives a first introduction to several areas that use algebraic structures and tools that may appear in models from various application domains.

Topics will include a broad range of areas like:


  1. Simple algebraic structures used in Cryptography (in particular for RSA).
  2. An introduction to group theory (for instance permutations) with basic examples in geometry, combinatorics coding theory and chemistry.
  3. Useful linear algebra tools with examples in coding theory or networks analysis.
  4. A short introduction to linear and Boolean optimisation including examples of models and basic tools for solving these models.
  5. A short introduction to network problems, in particular flows and/or walking problems.
  6. An introduction to Markov chains and their applications.

Maple, Mathematica and/or Excel will be used in lab sessions to illustrate the concepts and discuss their possible use in real life applications.

Objectives/Learning Outcomes/Capability Development


This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Mathematics) , BP245 Bachelor of Science (Statistics) and BH119 Bachelor of Analytics (Honours):

Knowledge and technical competence:

  • use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of delivery of the solution.

Teamwork and project management

  • contribute to professional work settings through effective participation in teams and organisation of project tasks
  • constructively engage with other team members and resolve conflict.


  • communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral) and tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • locate and use data and information and evaluate its quality with respect to its authority and relevance.


On completion of this course you should be able to:

  1. Identify and use basic algebraic structures and concepts that will be useful for more advanced courses in subsequent years
  2. Discriminate key principles of these algebraic concepts and show how they can be used in models from various applications;
  3. Demonstrate modelling and problem solving abilities at a basic level;
  4. Select and use, at a basic level, software tools that are suitable for working within these mathematical concepts.
  5. Communicate technical and non-technical material in a range of forms (written, oral, electronic, graphic,) and work effectively as a team member

Overview of Learning Activities


Key concepts will be explained and illustrated in detail in lectures. Supervised tutorials will build your capacity to use and apply the concepts for modelling and solving problems, to encourage you to think critically and analytically and provide feedback on your academic progress. Lab sessions will illustrate and experiment key notions using software such as Maple, Mathematica or Excel.

The assessment will be a combination of hand-written work; computer generated output, group work and end-of-semester hand-written exams.

Overview of Learning Resources


All course material will be provided online through myRMIT Studies. These resources will include lecture notes on selected topics, slides, articles, internet links and exercises. Some additional supporting documents can be found at

Overview of Assessment


Assessment Tasks:


Assessment Task 1: In-class tests

Two in-class tests (10% each) will assess your ability to apply analytic and problem solving skills.

Weighting 20%

This assessment task supports CLOs 1, 3

Assessment Task 2: Homework Assignments

Apply analytical and problem solving skills and use software tools.

Weighting 10%

This assessment task supports CLOs 1, 2, 3, 4

Assessment Task 3:  Group Project

The project allows you to explore in-depth one of the topics and present it to the class. It will assess technical skills as well as soft skills, in particular the ability to work in a team, to communicate and to use external sources of information.

Weighting 20%

This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment 4: Final Exam  

Weighting 50%

This assessment supports CLOs 1, 2, 3, 4