6 points, SCA Band 2, 0.125 EFTSL
Undergraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
- First semester 2019 (On-campus)
, , or equivalent
Vector spaces, linear transformations. Determinants, eigenvalue problems. Inner products, symmetric matrices, quadratic forms. LU-decomposition, least squares approximation, power method. Applications to coding, economics, networks, graph theory, geometry, dynamical systems, Markov chains, differential equations.
On completion of this unit students will be able to:
- Understand basic concepts related to vector spaces, including subspace, span, linear independence and basis;
- Understand basic properties of linear transformations and identify their kernel and range;
- Diagonalize real matrices by computing their eigenvalues and finding their eigenspaces;
- Understand basic concepts related to inner product spaces and apply these to problems such as least-squares data fitting;
- Apply tools from linear algebra in a wide variety of relevant situations;
- Understand and apply relevant numerical methods and demonstrate computational skills in linear algebra;
- Present clear mathematical arguments in both written and oral forms.
End of semester examination (3 hours): 60% (Hurdle)
Continuous assessment: 40% (Hurdle)
Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for both the end-of-semester examination and continuous assessment components.
Three 1-hour lectures and one 2-hour applied class per week
See also Unit timetable information