Optimisation techniques play an important role in practically all areas of data science, including data analysis and machine learning. An understanding of the core principles of optimisation algorithms and their properties is essential for all practitioners in this field.
You'll gain expertise in the theory and applications of optimisation techniques and algorithms, focusing on the methods most relevant to data science. You'll master how to optimise solutions, analyse and improve the performance of algorithms, and acquire decision-making techniques. The module will equip you with an understanding of the way many standard problems in data science can be formulated as optimisation problems. In addition, you’ll gain skills in applying basic optimisation algorithms and techniques, including Newton's and gradient based methods, to solve problems. Throughout the module computing tools will be used to illustrate how optimisation techniques and algorithms are used to compute solutions to relevant problems in data science.
Lecture 40, PC 12
2x report including code (20% each) worth 40%.
Assignment report including code worth 60%.
Reassessment Method: Like-for-like
Including composite form of reassessment for failed portfolio – written single report.
On successfully completing the module, students will be able to:
1) Critically appraise the well-established principles and methods of optimisation techniques in data science, and of the way those principles are built up and connected.
2) Apply the main methods of optimisation and ability to evaluate critically the appropriateness of different approaches to solving problems in data science.
3) Recognize the limits of the theory, and how this influences analyses and interpretations based on that knowledge.
4) Apply a range of established techniques to initiate and undertake critical analysis of information, and to propose optimization solutions to problems arising from that analysis.
5) Communicate their work and knowledge of optimisation accurately and using sound arguments.
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