MA12013: Core pure and statistical mathematics B
[Page last updated: 23 October 2023]
Academic Year: | 2023/24 |
Owning Department/School: | Department of Mathematical Sciences |
Credits: | 15 [equivalent to 30 CATS credits] |
Notional Study Hours: | 300 |
Level: | Certificate (FHEQ level 4) |
Period: |
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Assessment Summary: | CWSI 12%, EXCB 88% |
Assessment Detail: |
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Supplementary Assessment: |
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Requisites: | Before taking this module you must take MA12012 |
Learning Outcomes: |
By the end of the unit, you will be able to:
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Synopsis: | You will develop further skills in mathematics. You will study linear algebra, both via matrices and via vector spaces and linear maps, focussing on the most widely applicable aspects.
You will examine the theory of continuity for functions of one real variable. You will use continuous random variables and explore statistical modelling and parameter estimation.
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Content: | Algebra:
Vector spaces (over the rational, real, and complex numbers), linear maps, subspaces, kernel, and image. Linear independence, spans, bases, and dimension.
Matrix of a linear map, change of basis, linear operators, similar matrices. Rank, nullity and the Rank-Nullity theorem. Inner-product spaces: orthogonality, Cauchy-Schwarz inequality and applications. Gram-Schmidt process: orthonormal bases, orthogonal matrices and QR decomposition, orthogonal subspaces. Determinants and adjugates: properties and computation; invertibility of matrices. Eigenvalues, eigenvectors and eigenspaces; characteristic polynomial, algebraic and geometric multiplicities, diagonalisability. Self-adjoint operators, symmetric/Hermitian matrices and their orthogonal diagonalisation.
Probability & Statistics:
Properties of continuous random variables, common continuous distributions including the uniform, exponential, normal and gamma distributions. Transformations of random variables (RVs). Proof of the law of the unconscious statistician for a continuous RV. Joint probability density function (PDF). Marginal and conditional distributions of continuous RVs. Independence: factorisation of joint PDF as a product of marginals. Properties of covariance and correlation. Distribution of a sum of continuous RVs, including normal and exponential examples. Statement and application of the central limit theorem. Introduction to model fitting. Exploratory data analysis and model formulation.
Parameter estimation by the method of moments and maximum likelihood. Estimators as random variables. Sampling distributions of estimators. Bias, variance and mean-square error of an estimator. Graphical assessment of goodness of fit.
Functions:
Limits. Continuity. Differentiability. Real power series |
Course availability: |
MA12013 is Compulsory on the following courses:Department of Computer Science
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Notes:
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