This module aims to equip students with a comprehensive understanding of the theoretical and practical aspects of Optimisation. Students will be able to apply the fundamental concepts and advanced techniques with hands-on lab sessions. Students will gain a robust knowledge base and practical skills in applying optimisation methods to real-world problems.
A Understand the fundamental concepts of Optimisation such as being able to define and explain objective functions, constraints and optimality conditions, and understand different types of optimisation problems. B Apply optimisation techniques and algorithms effectively, including both classical and modern methods. C Implement optimisation algorithms to solve real-world problems. D Critically analyse optimisation results and evaluate the effectiveness and efficiency of different optimisation methods.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This philosophy is carried through in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The module will be delivered in a combination of lectures, seminars and labs. Lectures will introduce students to the academic content. Seminars and labs will be used to expand the students understanding of lecture materials. In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading. This module will leverage generative AI to enhance course content and teaching methods in line with the learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.