Machine Learning Thesis

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The curriculum is designed to propel your engineering or data science career forward, allowing you to choose the path that’s right for you, be that a role as a data scientist, a machine learning engineer, or a computational statistician.

With hands-on projects, you’ll build a portfolio to showcase your new skills in everything from probabilistic modeling, deep learning, unstructured data processing and anomaly detection.

You will graduate with an ability to go beyond the algorithms and turn data into actionable insights, contribute to strategic decision making in your organisation and become a responsible member of this rapidly growing profession.

Imperial, ranked #9 in the world by Times Higher Education, is home to numerous eminent world-famous researchers in machine learning, many of which will be contributing to this programme.

In data decomposition, we illustrate that a data set is best approximated by its principal subsets which are Pareto optimal with respect to the complexity and the set size.

In data pruning, we show that outliers usually have high complexity contributions, and propose methods for estimating the complexity contribution.This thesis summarizes four of my research projects in machine learning.One of them is on a theoretical challenge of defining and exploring complexity measures for data sets; the others are about new and improved classification algorithms.It has had a rich history in driving innovation since the beginning of this field: John Nelder, Professor at Imperial College, helped developed Gen Sim, the precursor to R and the first proper implementation of a general framework for regression.The university maintains close ties with industry and a number of pioneering tech companies, some of which will be contributing to the programme by way of project ideas for your MSc thesis.It is closely related to several existing principles used in machine learning such as Occam's razor, the minimum description length, and the Bayesian approach.We demonstrate the application of the data complexity in two learning problems, data decomposition and data pruning.Join a booming, in-demand field with a Master’s degree in Machine Learning from one of the top universities in the world.In this programme, you will develop an in-depth understanding of machine learning models, alongside invaluable practical skills and guided experience in applying them to real-world problems.Possibilities extend beyond this list, however, as machine learning is slowly becoming indispensable in other fields, such as journalism or even tourism.This is a rigorous programme: applicants are expected to have a quantitative undergraduate degree in a subject like computer science, math, statistics, economics, or physics.


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