|Delivery Type||Delivery length / details|
|Lecture||20 x 1 Hour Lectures|
|Assessment Type||Assessment length / details||Proportion|
|Semester Exam||2 Hours||60%|
|Semester Assessment||40 Hours Assignment||40%|
|Supplementary Assessment||40 Hours Assignment Students should resit failed components||40%|
|Supplementary Exam||2 Hours Students should resit failed components||60%|
On successful completion of this module students should be able to:
1. Recognise potentially difficult problems, compare and analyse different ways for solving them.
2. Develop an appropriate model as a linear program for a problem and solve it with the help of an LP solver.
3. Describe significant algorithms from the area of Advanced Reasoning and apply them appropriately.
4. Describe significant algorithms from the area of Bioinformatics and apply them appropriately.
5. Describe significant algorithms from the area of Intelligent Robotics and apply them appropriately.
6. Describe significant algorithms from the area of Vision, Graphics and Visualisation and apply them appropriately.
This module covers important, significant and non-trivial algorithms from different fields of research. The different fields covered are aligned with the research groups of the department, Advanced Reasoning, Bioinformatics and Computational Biology, Intelligent Robotics, and Vision, Graphics and Visualisation.
The module extends the algorithmic aspects of CC/CS21120. It introduces students to significant algorithms that are connected to different fields of research, each field aligned with an active research group in the department. This makes it an ideal preparation for major projects in Semester 2.
1. Introduction (2 lectures): difficult problems and how to deal with them (NP-hardness; approximation; heuristics; special cases; small instances; ‘efficient’
exponential time algorithms).
2. Linear programming (2 lectures): introduction, modelling, simplex algorithm, duality, LP solvers.
3. Selected topics from Advanced Reasoning (4 lectures): e.g., flow algorithms, streaming algorithms.
4. Selected topics from Bioinformatics (4 lectures): e.g., string algorithms (sequence alignment), hidden Markov models.
5. Selected topics from Intelligent Robotics (4 lectures): e.g., Kalman filters, particle filters.
6. Selected topics from Vision, Graphics and Visualisation (4 lectures), e.g., fast Fourier transform, scan line algorithm, morphing algorithms.
|Skills Type||Skills details|
|Application of Number||Inherent to linear programming.|
|Communication||Written communication skills as part of the assignment.|
|Improving own Learning and Performance||Usage of a significant software package as part of the assignment.|
|Information Technology||Inherent to the subject matter.|
|Personal Development and Career planning|
|Problem solving||Explicit in first topic; implicit in all algorithms.|
|Research skills||Usage of a significant software package as part of the assignment.|
|Subject Specific Skills||As laid out in the learning outcomes.|
This module is at CQFW Level 6