Data mining and knowledge discovery techniques are widely used in real-world applications. Examples of high-stakes applications include analysing data to decide whether or not a patient should undergo a surgery or analysing data to decide whether or not a customer should be granted a loan or hired for a job.
In this module you will learn in detail how data mining algorithms work to automatically extract knowledge from data, and why these algorithms – which are based mainly on machine learning (but also on statistics) – are so important for today's data-driven society. You will also learn about the broader process of knowledge discovery, which includes not only the application of data mining algorithms to real-world datasets, but also how to prepare data for the subsequent application of a data mining algorithm, and how to evaluate the knowledge discovered by a data mining algorithm.
This module emphasizes the use of techniques that learn predictive models that can be in principle interpreted by users, as opposed to machine learning techniques that learn black-box predictive models (not directly interpretable by users).
(Lectures) 32
Mini project worth 50%.
Take home test worth 50%.
Reassessment Method: Like-for-like Composite form of reassessment for failed components – mini project.
On successfully completing the module, students will be able to:
Appraise and analyse several types of algorithms for solving data mining task.
Critically evaluate the strengths and weaknesses of several types of data mining algorithms.
Appraise and analyse the knowledge discovery process, which includes data preprocessing methods, the application of data mining algorithms to real-world datasets, and methods for evaluating the quality of the knowledge discovered by data mining algorithms.
Critically evaluate the strengths and weaknesses of several types of methods for data preprocessing and discovered-knowledge evaluation.
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