Linear Algebra: 2019-2020
OverviewThe course will introduce basic concepts and techniques from linear algebra that will be required in later courses in areas such as machine learning, computer graphics, quantum computing.
The theoretical results covered in this course will be proved using mathematically rigorous proofs, and illustrated using suitable examples.
Computer Science and Philosophy students should note that the questions set on this course in the Final Honour School will be more challenging than those that are set for the Preliminary Examination in Computer Science.
At the end of this course the student will be able to:
- Comprehend vector spaces and subspaces.
- Understand fundamental properties of matrices including determinants, inverse matrices, matrix factorisations, eigenvalues and linear transformations.
- Solve linear systems of equations.
- Have an insight into the applicability of linear algebra.
Lectures 1-20 cover the syllabus for the Preliminary Examination in Computer Science.
Lectures 1-17 cover the syllabus for the Final Honour School in Computer Science and Philosophy.
Lectures 1-2 Vectors: Vectors and geometry in two and three space dimensions. Algebraic properties. Dot products and the norm of a vector. Important inequalities. Vector spaces, subspaces and vector space axioms. Complex vector spaces.
Lectures 3-5 Independence and orthogonality: Linear independence of vectors. Basis and dimension of a vector space. Orthogonal vectors and subspaces. The Gram-Schmidt orthogonalisation.
Lectures 6-8 Matrices: Matrix operations. Column, row and null space. Rank of a matrix. Inverse and transpose. Elementary matrices. The Gauss-Jordan method.
Lectures 9-11 Systems of linear equations: Examples of linear systems. Geometry of linear equations. Gaussian elimination. Row echelon form. Homogeneous and nonhomogeneous systems of linear equations. Application to the intersection of lines and planes.
- Lectures 12-14 Elementary matrix factorisations and determinants: LU factorisation, related algorithms and operation count. PLU factorisation. Calculating the determinant of a matrix. Properties of the determinant of a matrix. Application examples: area, volume and cross product.
- Lectures 15-17 Eigenvalues and eigenvectors: Definition. Similarity and diagonalisation.
Lectures 18-20 Linear transformations: Definition and examples. Properties and composition of linear transformations. Rotations, reflections and stretches. Translations using homogeneous coordinates. One-to-one and onto transformations.
- Vector spaces and subspaces
- Linear independence and bases for vector spaces
- Orthogonal vector spaces and the Gram-Schmidt orthogonalisation process
- Inverse matrices
- Solution of linear systems
- Elementary matrix factorisations
- Determinants of matrices
- Eigenvalues and eigenvectors
- Linear transformations (CS Prelims only)
- Introduction to Linear Algebra, Gilbert Strang, Wellesley-Cambridge press.