Linear Algebra introduces the core concepts of vectors and matrices (arrays), their arithmetic operation and the geometric and algebraic interpretation of these operations. This module provides essential knowledge for data analytics that is not available in the current leaving certificate curriculum.
Linear Systems.
Solving Linear Systems. Echelon Forms, Rank. Applications.
Matrices.
Matrix Algebra. Inverses. LU-Factorization. Applications.
Vectors.
Spaces of Vectors. Linear Independence, Bases, Dimension. Null Space, Column Space, Row Space. Linear Transformations on Rn.
Orthogonality.
Dot Product, Norm. Orthogonal Sets, Orthogonal Matrices. Orthogonal Subspaces, Projections, Bases. Applications.
Determinants.
Definition and Computation. Inverses and Products.
Eigenvalue Problems.
Eigenvalues and Eigenvectors. Diagonalization. Applied Eignevalue Problems. Markov Chains. Systems of Linear Differential Equations.
Vector Spaces.
Vector Spaces and Subspaces. Linear Independence, Basis, Dimension. Coordinates, Linear Transformations.
Learning will take place in different settings, lecture, tutorial and laboratory depending on the topic and the applications of that topic.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 70 |
| Other Assessment(s) | 30 |