- Knowledge of rounding error and costs of algorithms
- Knowledge of iterative methods for solving linear problems
- Knowledge of numerical eigenvalue/singular value problems
- Applications of these algorithms to examine, for example, Networks, Ranking, Compression, Optimization, Machine Learning
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This course will concentrate on the role of linear algebra in applied mathematics and provide a toolkit of techniques and examine their application. Linear algebra underpins most mathematical applications: from finding parameters in models to the algorithms used by google. We want efficient and accurate numerical methods. We will see how analysis and control of small errors can be crucial in avoiding large scale disasters. Examples of potential applications include analysis of networks, google page ranking and compression of data/images. The course is aimed at students in the second year and gives a flavour of different Applied Mathematics problems by building on linear algebra.
Instructional Modes
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The course is aimed at students in the second year and gives a flavour of different Applied Mathematics problems by building on linear algebra.
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| Linear Algebra A, Linear Algebra B |
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| This course will be taught in English. |
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