# COMPGV01 - Mathematical Methods Algorithms and Implementations

**This database contains the 2017-18 versions of syllabuses.** Syllabuses from the 2016-17 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) |
---|---|

Year | MSc |

Prerequisites | N/A |

Term | 1 |

Taught By | Bangti Jin (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. |

# Content

**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.

**Calculus**Ordinary differential equations (complementary functions and particular integrals); Partial differential equations (separation of variables);Vector and matrix calculus.

**Fourier Transforms**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.

# Assessment

The course has the following assessment components:

- Written Examination (2.5 hours, 75%)
- Examination rubric:
Answer THREE questions out of FIVE

All questions carry equal marks - Coursework Section (4 pieces of individual submission, 25%)
- Coursework 1 is due in Week 3
- Coursework 2 is due in Week 6
- Coursework 3 is due in Week 9
- Coursework 4 is due start of Term 2

To pass this course, students must:

- Obtain an overall pass mark of 50% for all sections combined.

# Resources

Reading list available via the UCL Library catalogue.