Machine Learning Thesis

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Imperial College London is the UK’s only university to focus solely on science, engineering, medicine, and business.

Consistently ranked amongst the top 10 universities in the world, Imperial is home to a global community of scientists, engineers, medics, and business experts.

This research-led approach shapes the way they educate students through teaching that opens everything up to question.

It’s a style of learning that relies on learning by discovery and prepares graduates to bring fresh perspectives to the ever-evolving landscape of technology.

Here’s a sample of Specializations on Coursera from other Imperial College programmes: This degree offers multiple pathways to meet the needs of students with multiple backgrounds -- both students just starting a career in data science, and those already working in roles such as senior data analysts, bioinformatics scientists, statisticians or business analysts.

Graduates are likely to pursue roles as data scientists, machine learning engineers, natural language processing engineers, data engineers, bioinformatics or health data scientists, AI engineers, or software engineers.

We first investigate the role of data complexity in the context of binary classification problems.

The universal data complexity is defined for a data set as the Kolmogorov complexity of the mapping enforced by that data set.

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.

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