# Linear Algebra (MAST10007)

Undergraduate level 1Points: 12.5On Campus (Parkville)

Or view archived Handbooks
You’re currently viewing the 2017 version of this subject

## Overview

Year of offer 2017 Undergraduate Level 1 MAST10007 Parkville Summer TermSemester 1Semester 2 Subject EFTSL, Level, Discipline & Census Date

This subject gives a solid grounding in key areas of modern mathematics needed in science and technology. It develops the concepts of vectors, matrices and the methods of linear algebra. Students should develop the ability to use the methods of linear algebra and gain an appreciation of mathematical proof. Little of the material here has been seen at school and the level of understanding required represents an advance on previous studies.

Systems of linear equations, matrices and determinants; vectors in real n-space, cross product, scalar triple product, lines and planes; vector spaces, linear independence, basis, dimension; linear transformations, eigenvalues, eigenvectors; inner products, least squares estimation, symmetric and orthogonal matrices.

## Intended learning outcomes

Students completing this subject should:

• be able to use matrix techniques to represent and solve a system of simultaneous linear equations;
• understand the use of vectors in describing lines and planes in solid geometry;
• understand the extension of vector concepts to abstract vector spaces of arbitrary finite dimension;
• understand linear transformations, their matrix representations and applications;
• become familiar with the use of a computer package for symbolic and numeric calculation.

## Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

• problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
• analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
• collaborative skills: the ability to work in a team;
• time-management skills: the ability to meet regular deadlines while balancing competing commitments; and
• computer skills: the ability to use mathematical computing packages.

Last updated: 12 February 2018