Mathematics for Computer Science and Philosophy: 20222023
Lecturers  
Degrees 
Overview
This is a secondyear course for Computer Science & Philosophy students. They will attend a subset of the firstyear lectures in Linear Algebra and Continuous Mathematics, and will so be equipped with the knowledge needed for courses in Machine Learning.
This course is part of both the Preliminary Examination for Computer Science students and the Final Honour School for Computer Science and Philosophy students. Students should note that the questions set on this course in 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 should bear this in mind when attempting sample exam questions and past exam questions.
Classes: Colleges will arrange problem classes for their students, as with the corresponding firstyear courses: three classes for Linear Algebra and two for Continuous Mathematics.
Examinations: The FHS examination will consist of three questions, of which you should answer two. One question will be on Linear Algebra, one on Continuous Mathematics, and one will be on either one of these topics or a combination of both topics.
Synopsis
Linear Algebra
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.
Continuous Mathematics
*[3 lectures] Derivatives, partial derivatives, differentiation with respect to a vector, gradient, Hessian and Jacobian. Taylor's theorem in 1 dimension (Lagrange remainder), Taylor's theorem in n dimensions (remainder only briefly). Examples.
*[3 lectures] Optimization in 1 and n dimensions. Classification of turning points via Taylor's theorem. Convexity. Constrained optimization: Lagrange multipliers. Examples.
[23 lectures] Numerical integration in 1 dimension: midpoint and Simpson’s rules, complexity and error analysis. Briefly, integration in n dimensions and Monte Carlo methods. Examples.
*[1 lecture] Floatingpoint numbers and rules of thumb for accuracy in practice. Convergence rates of iterative methods.
*[23 lectures] Numerical root finding in 1 dimension by bisection, Newton's method, secant method. Root finding in n dimensions by Newton's method, and briefly quasiNewton methods. Complexity and error analysis. Examples.
[23 lectures] Numerical optimization in 1 and n dimensions: root finding for gradient and gradient descent methods. Complexity and error analysis. Examples.
[12 lectures] Applications.
Sections marked * cover the syllabus for the Continuous Maths part of Mathematics for Computer Science and Philosophy.
Syllabus
Vector spaces and subspaces. Matrices. Inverse matrices. Solution of linear systems. Elementary matrix factorisations. Eigenvalues and eigenvectors.
Derivatives, partial derivatives, differentiation with respect to a vector; gradient, Hessian, Jacobian. Taylor’s theorem in 1 and ndimensions, with Lagrange remainder. Optimization in 1 and ndimensions, classification of stationary points. Constrained optimization and the method of Lagrange multipliers. Iterative numerical methods and rates of convergence. Methods for numerical root finding in 1 and ndimensions, complexity and (simple cases of) error analysis.
Reading list
Linear Algebra: Introduction to Jim Hefferon, Linear Algebra
Continuous Maths: Please see the course materials page for Continuous Maths.
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.
Taking our courses
This form is not to be used by students studying for a degree in the Department of Computer Science, or for Visiting Students who are registered for Computer Science courses
Other matriculated University of Oxford students who are interested in taking this, or other, courses in the Department of Computer Science, must complete this online form by 17.00 on Friday of 0th week of term in which the course is taught. Late requests, and requests sent by email, will not be considered. All requests must be approved by the relevant Computer Science departmental committee and can only be submitted using this form.