COMPGV01 - Mathematical Methods Algorithms and Implementations
This database contains the 2016-17 versions of syllabuses. Syllabuses from the 2015-16 session are available here.
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
|Code||COMPGV01 (Also taught as: COMPM072)|
|Taught By||Dan Stoyanov (100%)|
|Aims||To provide a rigorous mathematical approach: in particular to define standard notations for consistent usage in other modules. To present relevant theories and results. To develop algorithmic approach from mathematical formulation through to hardware implications.|
|Learning Outcomes||To understand analytical and numerical methods for image processing, graphics and image reconstruction.|
Linear Algebra via Geometry
Vectors and matrices; Eigenvalues; Kernel spaces; Singular value decomposition; Coordinate systems, lines, planes, rotation and translation.
Probability and Estimation
Forward probability; Common probability distributions; Monte Carlo sampling; Moments; Inverse probability; Bayes Theorem; Maximum likelihood estimation.
Ordinary differential equations (complementary functions and particular integrals); Partial differential equations (separation of variables);Vector and matrix calculus.
Calculating Fourier series and transforms; Discrete and Fast Fourier Transforms.
Basic Algorithms and Optimization
Dynamic programming; Gradient Descent; Gauss-Newton.
Method of Instruction:
Lecture presentations with associated class coursework and laboratory sessions. There are 4 pieces of coursework, all equally weighted.