Machine Learning Thesis

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.

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.

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.

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.Experiments were carried out with a practical complexity measure on several toy problems.We then propose a family of novel learning algorithms to directly minimize the 0/1 loss for perceptrons.The title of this degree programme and the credential awarded for its successful completion is subject to final approval by Imperial College London.Sign up to receive updates on this exciting new 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.You will build a strong foundation in mathematics and statistics, giving you confidence in your analytical skills, but also acquire expertise in implementing scalable machine learning solutions using industry-standard tools such as Py Spark, ensuring that no data is too big or too complex for you.You will also have the opportunity to broaden your horizons through one of the first of its kind study of ethical topics posed by machine learning.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.

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