Course Catalogue

Linear Algebra and Space Analytic Geometry I (UESTC) UESTCHN1001

  • Academic Session: 2026-27
  • School: School of Engineering
  • Credits: 14
  • Level: Level 1 (SCQF level 7)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

This course introduces the fundamental concepts, methods and theories of linear algebra, vector spaces and quadratic forms.

Timetable

Course will be delivered continuously in the traditional manner at UESTCHN.

Requirements of Entry

Mandatory Entry Requirements

None

Recommended Entry Requirements

None

Excluded Courses

None

Co-requisites

None

Assessment

Assessment

Examination 75% - closed book mid term 20%/ closed book final exam55%

Project work 25%

Main Assessment In: April/May

Course Aims

This course aims to provide a foundation in linear algebra, including basic understanding of linear systems, matrix algebra, determinants, vector spaces, eigenvalue problems, orthogonality and quadratic forms, to prepare the students for their future study and research.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Apply Gaussian elimination solve linear systems and perform matrix algebra;

■ Apply the properties of determinants to evaluate the determinant of a matrix, and apply Cramer's rule to solve linear systems;

■ Evaluate linear dependence of a vector set, establish a basis, evaluate the dimension and coordinates for an n-dimensional vector space, apply basis and dimension evaluation to the fundamental subspaces of a matrix;

■ Apply characteristic polynomial to solve the eigenvalue problem and diagonalization;

■ Formulate inner products, orthogonality and apply the Gram-Schmidt process to construct an orthogonal basis, apply orthogonal projection to find the best approximation;

■ Apply change of variable to transform a quadratic form into one with no cross-product term and orthogonally diagonalize a symmetric matrix.

Minimum Requirement for Award of Credits

No exceptions