Machine learning algorithms are employed in a myriad of applications in almost any domain, being it industrial, commercial, scientific or academic. Many techniques have been developed to tackle such a diversity of applications. There are, however, very few comparative studies of these algorithms, most of them limited to a specific problem and its related datasets. Therefore, practitioners do not have well supported information on what algorithms to use for their problems, having the burden of considering many algorithms, often without in-depth knowledge about their underlying technical details.
This project aims to go beyond these very few comparative studies, and to develop a systematic analysis and benchmarking of the widely-used machine learning algorithms and their implementations. The study will be generic, meaning it will not be problem/domain specific.
The student working on this project will have the opportunity to develop knowledge and skills related to a broad range of machine learning algorithms, techniques and tools. Such core abilities will result in a high employability level having the flexibility of applying them in any industry or domain.
Applicants should have (or about to obtain) a first or second class UK honours undergraduate or master degree, or a recognised equivalent from an overseas university in a discipline such as computer science, computer engineering, statistics, mathematics, physics, or other related areas. A solid background in computer programming is essential.
Funding Notes
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: View Website. Recently the UK Government made available the Doctoral Student Loans of up to £25,000 for UK and EU students and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.)
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