Linear Algebra: 20192020
Lecturer  
Degrees  
Term  Michaelmas Term 2019 (20 lectures) 
Overview
The 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.
Learning outcomes
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.
Synopsis
Lectures 120 cover the syllabus for the Preliminary Examination in Computer Science.
Lectures 117 cover the syllabus for the Final Honour School in Computer Science and Philosophy.

Lectures 12 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 35 Independence and orthogonality: Linear independence of vectors. Basis and dimension of a vector space. Orthogonal vectors and subspaces. The GramSchmidt orthogonalisation.

Lectures 68 Matrices: Matrix operations. Column, row and null space. Rank of a matrix. Inverse and transpose. Elementary matrices. The GaussJordan method.

Lectures 911 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 1214 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 1517 Eigenvalues and eigenvectors: Definition. Similarity and diagonalisation.

Lectures 1820 Linear transformations: Definition and examples. Properties and composition of linear transformations. Rotations, reflections and stretches. Translations using homogeneous coordinates. Onetoone and onto transformations.
Syllabus
 Vector spaces and subspaces
 Linear independence and bases for vector spaces
 Orthogonal vector spaces and the GramSchmidt orthogonalisation process
 Matrices
 Inverse matrices
 Solution of linear systems
 Elementary matrix factorisations
 Determinants of matrices
 Eigenvalues and eigenvectors
 Linear transformations (CS Prelims only)
Reading list
 Introduction to Linear Algebra, Gilbert Strang, WellesleyCambridge press.