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Curricular information is subject to change
On completion of this module, students should be able to:
1. Understand the basic optimisation techniques
2. Model real-world problems in terms of linear programming and integer linear programming
3. Competently apply the basic optimisation techniques to solve problems in various domains, including machine learning
4. Gain a fundamental understanding of convex optimisation and gradient-based approaches
Fundamentals of Optimisation
Linear Programming (Simplex Method)
Integer Programming
NP-Hardness and Approximation Algorithms
Meta-heuristics (e.g., Genetic programming, Simulated Annealing)
Combinatorial Optimisation
Convex Optimisation (including gradient-based optimisation)
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 24 |
Autonomous Student Learning | 80 |
Total | 128 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment(Including Essay): Assignment to assess if a student is able to model a real-world problem into LP or ILP, use a solver to solve that formulation. Also, there can be some questions on convex optimisation. | n/a | Alternative linear conversion grade scale 40% | No | 20 |
|
Assignment(Including Essay): Assignment to assess if a student is able to model a real-world problem into a LP or ILP, use a solver to solve that formulation showing an understanding of how ILPs are solved. | n/a | Alternative linear conversion grade scale 40% | No | 30 |
|
Exam (In-person): An in-person end of trimester examination consisting of longer questions to test the understanding of optimisation concepts as well as short answer questions | n/a | Alternative linear conversion grade scale 40% | No | 50 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Formative assessment in tutorial sessions; For the assignments, a group feedback will be provided post-assessment and an individual feedback will be posted on the Brightspace VLE later.
Name | Role |
---|---|
Mr Jiwei Zhang | Tutor |