Core Modules 
Applied Mathematics Courses 
Mathematics Courses 
Advanced Linear Algebra 
Machine Learning 
Algebraic Graph Theory 
Calculus on Manifolds 
Neural Networks 
Enumerative Combinatorics 

Numerical Modelling 
Introduction to Category Theory 

Spectral Collocation Method 
Measure Theory 


Topics in Algebra and/or Geometry 


Topology 
Advanced Linear Algebra (Prof Andriantiana):
This course starts by revisiting, in a more general setting, topics seen in MAM202 Linear Algebra, such as the determinant, linear independence, trace and eigenvalues. Then we treat new topics such as eigenspace, algebraic and geometric multiplicities of eigenvalues, characteristic polynomials and the re lations between them. We explore wellknown theorems from linear algebra such as the Diagonalisation Theorem, the Perron–Frobenius Tehorm and the Rayleigh Theorem for eigenvalues. The course con cludes with examples of applications in combinatorics and graph theory.
Prerequisites. MAM202 or an approved equivalent course covering basic linear algebra.
Algebraic Graph Theory (Prof Andriantiana):
This course introduces graph theory by studying basic properties of graphs. For this it covers the notion of homomorphism, transitivity, and spectra of graphs. The main part of the course consists of studying adjacency matrices of graphs. This involves looking at characteristic polynomial and spectrum of the adjacency matrices and using them for combinatoric purpose, to extract information about the graph. Laplacian matrices of graph will be considered as well. As for adjacency matrices, relations between their characteristic polynomials and spectra to properties of graphs will also be analysed. The last part of the course is on applications of graph theory to chemistry, physics and computer science.
Calculus on Manifolds (Prof. Pollney):
This course introduces mathematics on curved spaces in Ndimensions. We review concepts of multi variable calculus, such as coordinates, vectors and derivatives, but update and generalize these notions for broader application. We introduce the dual space of 1forms and define multilinear maps called tensors. We then extend Euclidean flatspace definitions to their natural analogies on curved surfaces. Partial derivatives are generalized by the covariant derivative and the notion of curvature is developed and explored. The geodesic equation is used as both a motivating calculation and a tool for understanding these new concepts.
Prerequisites. MAM2 Linear Algebra and MAM2 Multivariable Calclulus
Enumerative Combinatorics(Prof Burton):
Dealing a hand of cards, throwing a pair of dice, selecting a handful of marbles from a bag of marbles of various colours, painting the vertices of a regular solid with a selection of colours are all processes, which we shall call experiments, with observable outcomes.
The objective of this course is to develop techniques for counting the number of ways in which outcomes with certain properties can occur. An outline of the course is as follows.
The course will develop problemsolving skills and the results that we prove will have a wide range of applications. Some typical problems:
Introduction to Category Theory (Mr Nxumalo):
Category theory provides an absolute structure for comparing different branches of mathematics and also develops general constructions which can be used to describe and formalize abstraction of other mathematical concepts such as sets, groups and topology.
This is an introductory category theory course which aims to introduce basic notions of category theory, their primary properties and examples (predominantly in sets, algebra and topology).
Some of the topics covered include: Fundamentals of categories; Subcategories; Functors and natural transformations; Yoneda Lemma and Yoneda embedding; Objects and morphisms in abstract categories; Products and coproducts; Limits and colimits; Pullbacks and pushouts; Factorization structures; Adjoint functors; Adjoint situations; Monads.
Prerequisite: MAT3 or equivalent course.
Machine Learning (Dr Atemkeng):
This course will cover topics on Dimensionality reduction (SVD, PCA, Autoencoders), clustering algorithms ( Kmeans, Hierarchical Clustering, Gaussian Mixture model), Ensemble methods (Supervised decision trees and random forests, Unsupervised random forests), Outlier detection, CrossValidation and an introduction to Artificial Neural Network. Each aspect of the course will be linked to a practical and theory assessment.
Topics in Algebra and/or Geometry (Prof Remsing):
Measure theory (Dr Pinchuck):
We begin by study Riemann integration and some of its shortcomings. In order to overcome many of these difficulties, we introduce the concept of a measure space. In particular, we focus on the Lebesgue measure on a Euclidean space which assigns a conventional length, area and volume of Euclidean geometry. This leads to an improved theory of integration of realvalued functions. We will cover the important theorems in measure theory and emphasis will be put on solving various problems involving measure and integration.
Prerequisite: 3rd year real analysis or equivalent.
Neural Networks (Prof. Burton):
An artificial neural network (ANN) is a parallel computational system which consists of layers of neurons (which are just functions, called transfer functions) and connections between the layers (the connections are achieved by means of matrices of adjustable parameters). The parameters are adjusted in a way which roughly resembles the way in which neurotrasmitters in the brain are adjusted during learning.
The study of ANNs is heavily dependent on linear algebra and multivariate calculus and some basic concepts from statistics. The course will develop as follows.
• Regression and beyond
• Perceptrons: These simple ANNs were introduced in the 1950s and were the first real attempt to get a computer to classify patterns. However, they were not able to classify certain patterns and the initial excitement died down. Although they are not used anymore they are worth studying as an introduction to neural computing.
• Multi–layer, feed–forward networks with differentiable transfer functions: The machine learning pioneer, Paul Werbos, used a crucial notion, called backpropagation, to adjust the parameters in a neural network. This put ANNs back on the map and the study of artificial intelligence took off. We will study this in depth and construct ANNs from scratch using the MATLAB computational environment. With this experience behind us we will explore the MATLAB Deep Learning toolbox and use this to solve complex pattern recognition and prediction problems.
• Radial basis function networks (RBF): These ANNs are very different from feed–forward networks but have been shown to be equivalent to them. We will construct these RBF networks to solve complex problems.
• Dynamic Networks: These networks are deployed for time–series problems. We will develop our own methods, as well as the MATLAB toolbox, to construct and deploy them to solve time–series problems.
• Competitive Learning: The neurons in a competitive layer compete, in a sense which will be made precise, for the input data points. An updating procedure clusters the input data points such that input points in a cluster are similar to each other and dissimilar from data points in other clusters. In this way, the set of input data points are classified. This is an example of unsupervised learning. We will do this from first principles and also with the MATLAB Deep Learning toolbox.
Numerical Modelling (Prof. Pollney):
Partial differential equations arise in a number of applications modelling realworld phenomena. However, nonlinear equations do not lend themselves easily to analytic solution so that computer methods are necessary to model their behaviour. This course develops advanced numerical methods for solving evolution problems, in particular hyperbolic and parabolic PDEs. We introduce discrete approximations to continuous equations, study potential sources of error, and develop advanced techniques from the method of characteristics in order to approximate generic phenomena such as shocks.
Prerequisite: MAP311 Numerical Analysis, MAP314 Partial Differential Equations.
Spectral Collocation Method (Dr Oloniiju):
This course will cover the fundamentals of spectral and pseudospectral collocation methods, emphasizing implementation in Python or MATLAB. Spectral methods are powerful computational methods for solving differential equations that arise in applied sciences. Compared to other traditional numerical methods, spectralbased methods have been reported to be more accurate, particularly for problems with smooth solutions. This course gives students a thorough foundation and offers a handson approach to applying and implementing spectral methods. The course will cover topics in: monomial and Lagrange interpolation on equal and unequal grids; collocation with monomial, Chebyshev and Lagrange polynomials as basis function; matrixbased approach to collocation and interpolation; domain decomposition for timedependent differential equations; linearization techniques for nonlinear problems and computing eigenvalues of linear boundary value problems.
Prerequisites: Numerical Analysis, Numerical Programming with MATLAB/Python, Differential Equations.
Topology (Dr Mclean):
Topology is the study of geometrical properties and spatial relations that are preserved by the continuous change of shape or size of objects. Covered topics: Finite products of spaces; Infinite products of spaces; Quotient spaces; Topological convergence (via filters and ultrafilters); Separation axioms (regular, complete regular, Tychonoff and normal spaces); Urysohn's lemma; Compactness (compact spaces, locally compact spaces and compactifications); The Tychonoff theorem; Metrizable spaces; Connectedness (connected spaces, pathwise connectedness, local connectedness); The fundamental group (homotopy, the fundamental group); Concept of manifolds.
Prerequisite: MAT3 or equivalent course.
Last Modified: Wed, 08 Mar 2023 17:15:50 SAST