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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: Assignment to assess if a student is able to model a real-world problem into a linear program and then use a solver to solve that formulation. | Throughout the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 25 |
Assignment: Assignment to assess if a student is able to model a real-world problem into integer linear program, use a solver to solve that formulation showing an understanding of how ILPs are solved | Throughout the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 25 |
Examination: An in-person end of trimester examination is currently planned for this module. These arrangements are subject to COVID-19 public health advice and may change during the trimester. | 2 hour End of Trimester Exam | Yes | 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 |
---|---|
Zhonghe Chen | Tutor |
Mr Eanna Curran | Tutor |
Sukriti Dhang | Tutor |
Mr Jiwei Zhang | Tutor |
Tutorial | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 12:00 - 13:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Tues 13:00 - 13:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 30, 31, 32, 33 | Tues 16:00 - 16:50 |