Linear Algebra: 20222023
Lecturer  
Degrees  
Term  Michaelmas Term 2022 (20 lectures) 
Overview
The course introduces 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. The syllabus for the Preliminary Examination in Computer Science changed in the academic year 20192020 and then again in 20212022.
This course is part of both the Preliminary Examination for Computer Science students and the Final Honour School for Computer Science and Philosophy students. Questions set from this course for the Final Honour School in Computer Science and Philosophy will be more challenging than those that are set for the Preliminary Examination in Computer Science, and students taking this exam should bear this in mind when attempting sample exam questions and past exam questions.
Learning outcomes
At the end of this course the student will be able to:
 Solve linear systems of equations.
 Comprehend vector spaces and subspaces.
 Understand fundamental concepts of linear maps, including isomorphisms, range and nullspace, matrices, change of bases and projections.
 Deal with Determinants.
 Understand similarity, including eigenvalues, eigenvectors, and diagonalisation.
 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. They follow closely the corresponding chapters from the textbook.

Lectures 13 Linear Systems: solving linear systems; linear geometry; reduced echelon form.

Lectures 47 Vector Spaces: definition; linear independence; basis and dimension.

Lectures 813 Maps Between Spaces: isomorphisms; homomorphisms; computing linear maps; matrix operations; change of basis; projection.

Lectures 1415 Determinants: definition; geometry of determinants; Laplace's formula.
 Lectures 1617 Similarity: definition; eigenvectors and eigenvalues.

Lectures 1820 Least Squares and Factorisations: least squares; LU factorisation; QR factorisation; singular value decomposition.
Syllabus
 Linear systems
 Reduced echelon form
 Vector spaces
 Linear independence
 Basis and dimension
 Linear maps
 Isomorphism
 Range and null space
 Matrices
 Change of basis
 Orthogonal projection
 Determinants
 Laplace's formula
 Similarity
 Eigenvalues and eigenvectors
 Diagonalisation
 Least squares (CS Prelims only)
 LU factorisation, QR factorisation, singular value decomposition (CS Prelims only)
Reading list
 Jim Hefferon. Linear Algebra.
Feedback
Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.