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MSc in Mathematical Sciences
Mathematical and Theoretical Physics
MSc in Mathematical Modelling and Scientific Computing
MSc in Mathematics and Foundations of Computer Science
MSc in Mathematical Finance (28m)
Industrially Focused Mathematical Modelling
PDE CDT Programme
Graduate Courses
MSc in Mathematical and Computational Finance
CDT in Mathematics of Random Systems
MSc in Mathematical Sciences
Schedule of units for course:
MSc in Mathematical Sciences (OMMS) 2019-20
Michaelmas
C1.1 Model Theory
C1.3 Analytic Topology
C2.1 Lie Algebras
C2.2 Homological Algebra
C2.4 Infinite Groups
C2.7 Category Theory
C3.1 Algebraic Topology
C3.3 Differentiable Manifolds
C3.4 Algebraic Geometry
C3.8 Analytic Number Theory
C4.1 Further Functional Analysis
C4.3 Functional Analytic Methods for PDEs
C4.8 Complex Analysis: Conformal Maps and Geometry
C5.1 Solid Mechanics
C5.5 Perturbation Methods
C5.7 Topics in Fluid Mechanics
C5.11 Mathematical Geoscience
C5.12 Mathematical Physiology
C6.1 Numerical Linear Algebra
C6.3 Approximation of Functions
C6.5 Theories of Deep Learning
C7.1 Theoretical Physics (C6)
C7.5 General Relativity I
C8.1 Stochastic Differential Equations
C8.3 Combinatorics
Hilary
C1.2 Godel's Incompleteness Theorem
C1.4 Axiomatic Set Theory
C2.3 Representation Theory of Semisimple Lie Algebras
C2.5 Non-Commutative Rings
C2.6 Introduction to Schemes
C3.2 Geometric Group Theory
C3.5 Lie Groups
C3.7 Elliptic Curves
C3.9 Computational Algebraic Topology
C3.10 Additive and Combinatorial Number Theory
C4.6 Fixed Point Methods for Nonlinear PDEs
C5.2 Elasticity and Plasticity
C5.4 Networks
C5.6 Applied Complex Variables
C5.9 Mathematical Mechanical Biology
C6.2 Continuous Optimisation
C6.4 Finite Element Method for PDEs
C7.1 Theoretical Physics (C6)
C7.4 Introduction to Quantum Information
C7.6 General Relativity II
C8.2 Stochastic Analysis and PDEs
C8.4 Probabilistic Combinatorics
C8.5 Introduction to Schramm-Loewner Evolution
C8.6 Limit Theorems and Large Deviations in Probability
Mathematical and Theoretical Physics
Michaelmas
Quantum Field Theory
Groups and Representations
Topological Quantum Theory
An Introduction to LaTeX
Soft Matter Physics
Kinetic Theory
Quantum Processes in Hot Plasma
Advanced Quantum Theory
C7.5 General Relativity I
C5.5 Perturbation Methods
C6.1 Numerical Linear Algebra
C3.1 Algebraic Topology
C3.4 Algebraic Geometry
C3.3 Differentiable Manifolds
Advanced Philosophy of Physics
Hilary
String Theory I
Radiative Processes and High Energy Astrophysics
Cosmology
Soft Matter Physics
Galactic and Planetary Dynamics
Nonequilibrium Statistical Physics
Collisionless Plasma Physics
Advanced Fluid Dynamics
Symbolic, Numerical and Graphical Scientific Programming
Advanced Quantum Field Theory
C5.6 Applied Complex Variables
C3.2 Geometric Group Theory
C7.6 General Relativity II
C5.4 Networks
C7.4 Introduction to Quantum Information
Supersymmetry and Supergravity
Advanced Philosophy of Physics
High Energy Density Physics
Renormalisation Group
Astroparticle Physics
Advanced Supersymmetry
Geophysical Fluid Dynamics
Trinity
String Theory II
Conformal Field Theory
Radiative Processes and High Energy Astrophysics
Quantum Matter: Superconductors, Superfluids, and Fermi Liquids
Topics in Soft and Active Matter Physics
Quantum Field Theory in Curved Space-Time
Dissertation
The Standard Model and Beyond I
Disc Accretion in Astrophysics: Theory and Applications
The Standard Model and Beyond II
Advanced Topics in Plasma Physics
MSc in Mathematical Modelling and Scientific Computing
Michaelmas
C6.3 Approximation of Functions
B5.5 Further Mathematical Biology
B6.3 Integer Programming
C5.11 Mathematical Geoscience
C5.12 Mathematical Physiology
C5.5 Perturbation Methods
C5.1 Solid Mechanics
C8.1 Stochastic Differential Equations
C6.5 Theories of Deep Learning
C5.7 Topics in Fluid Mechanics
B5.3 Viscous Flow
B5.2 Applied Partial Differential Equations
Supplementary Applied Mathematics
B6.1 Numerical Solution of Differential Equations I
C6.1 Numerical Linear Algebra
Mathematical Modelling
Practical Numerical Analysis
Additional Skills
Hilary
C5.6 Applied Complex Variables
C3.9 Computational Algebraic Topology
C5.2 Elasticity and Plasticity
C6.4 Finite Element Method for PDEs
C5.9 Mathematical Mechanical Biology
B8.3 Mathematical Models of Financial Derivatives
C5.4 Networks
B6.2 Numerical Solution of Differential Equations II
B5.1 Stochastic Modelling of Biological Processes
B5.4 Waves and Compressible Flow
Case Studies in Mathematical Modelling
Case Studies in Scientific Computing
B5.6 Nonlinear Systems
Further Partial Differential Equations
Further Mathematical Methods
C6.2 Continuous Optimisation
Trinity
C++ for Scientific Computing
Python in Scientific Computing
MMSC Dissertations
MSc in Mathematics and Foundations of Computer Science
Michaelmas
C3.1 Algebraic Topology
C1.3 Analytic Topology
B2.1 Introduction to Representation Theory
C2.1 Lie Algebras
C1.1 Model Theory
B3.5 Topology and Groups
C3.4 Algebraic Geometry
C2.2 Homological Algebra
Categories, Proofs and Processes
Computer Aided Formal Verification
Foundations of Computer Science
B8.5 Graph Theory
Introduction to Cryptology
C8.3 Combinatorics
Computational Game Theory
C3.8 Analytic Number Theory
C2.4 Infinite Groups
Computational Learning Theory
Quantum Computer Science
B6.3 Integer Programming
C2.7 Category Theory
C3.3 Differentiable Manifolds
B8.4 Information Theory
Hilary
C3.7 Elliptic Curves
B3.4 Algebraic Number Theory
B2.2 Commutative Algebra
C1.2 Godel's Incompleteness Theorem
Lambda Calculus and Types
C1.4 Axiomatic Set Theory
C2.6 Introduction to Schemes
C2.5 Non-Commutative Rings
C3.2 Geometric Group Theory
C2.3 Representation Theory of Semisimple Lie Algebras
Analysing Logics using Tree Automata
Categorical Quantum Mechanics
C3.9 Computational Algebraic Topology
Distributional Models of Meaning
C5.4 Networks
C8.4 Probabilistic Combinatorics
Probability and Computing
C3.10 Additive and Combinatorial Number Theory
C3.5 Lie Groups
Automata, Logic and Games
Computational Complexity
Applied Category Theory
Trinity
Concurrency
MFoCS Dissertations
Topological Groups
MSc in Mathematical Finance (28m)
Michaelmas
Module 5: Advanced Modelling Topics 1
Module 6: Advanced Numerical Methods
Dissertations - MSc in Mathematical Finance
Hilary
Module 1: Mathematical and Technical Prerequisites
Module 2: Black Scholes Theory
Module 3: Extensions of the Black Scholes Theory
Module 7: Advanced Modelling Topics 2
Module 8: Quantitative Risk Management
Dissertations - MSc in Mathematical Finance
Trinity
Module 4: Exotic Options and Advanced Modelling Techniques
Dissertations - MSc in Mathematical Finance
Industrially Focused Mathematical Modelling
Michaelmas
Core01: Mathematical Modelling
Core02: Scientific Computing
Core03: Modelling, analysis and computation of continuous real world problems
Core04: Computational Techniques
Core05: Mathematical Analytics
Core06: Optimisation
Hilary
C3.5 Lie Groups
C3.9 Computational Algebraic Topology
C4.6 Fixed Point Methods for Nonlinear PDEs
C5.2 Elasticity and Plasticity
C5.4 Networks
C5.6 Applied Complex Variables
C5.9 Mathematical Mechanical Biology
C6.4 Finite Element Method for PDEs
C8.2 Stochastic Analysis and PDEs
C8.4 Probabilistic Combinatorics
ModCase: Modelling Case Studies
SciCase: Scientific Computing Case Studies
PDE CDT Programme
Graduate Courses
Michaelmas
Scientific Computing for DPhil students I
Hilary
Scientific Computing for DPhil students II
MSc in Mathematical and Computational Finance
Michaelmas
Introduction to Partial Differential Equations
Introduction to Probability
Introduction to Statistics
Numerical Methods
Stochastic Calculus
Financial Derivatives
Statistics and Financial Data Analysis
Financial Computing with C++ Part I
Python
An Introduction to LaTeX
Financial Markets and Instruments
Markdown reports
Hilary
Stochastic Volatility
Fixed Income
Asset Pricing
Market Microstructure and Algorithmic Trading
Machine Learning
Optimisation
Stochastic Control
Financial Computing with C++ II
Quantitative Risk Management
Advanced Monte Carlo Methods
Advanced Numerical Methods
Trinity
Dissertation
CDT in Mathematics of Random Systems
Michaelmas
C6.5 Theories of Deep Learning
Simulation Methods and Stochastic Algorithms
Foundations of Stochastic Analysis
Foundations of Data Science
Function Spaces and Distribution Theory
Programming in Python
Hilary
C6.2 Continuous Optimisation
C8.2 Stochastic Analysis and PDEs
C8.4 Probabilistic Combinatorics
C8.5 Introduction to Schramm-Loewner Evolution
C8.6 Limit Theorems and Large Deviations in Probability