Introductory description

This module introduces students to the theoretical foundations and key algorithms of modern machine learning.

Pre-requisites

• Statistics students. ST231 Linear Statistical Modelling with R .
• Non-statistics students. ST240 Linear Statistical Modelling or ST351 Linear Statistical Modelling (For Finalists).

This module is available for students on a course where it is a listed option and as an Unusual Option to students who have the required background as covered by the pre-requisite modules.

Pre-registration. There is a cap on student numbers for this module and pre-registration is essential. Information about prioritisation and the pre-registration form can be found at http://go.warwick.ac.uk/ST340

Leads to: ST349 Machine Learning Frameworks, ST420 Statistical Learning and Big Data.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module will cover many contemporary data analysis problems and algorithms.

1. Fundamentals of statistical learning: Supervised vs unsupervised, regression vs classification, over- vs under-fitting, curse of dimensionality, training vs testing, validation and cross-validation, regularisation.

2. Supervised learning methods. An indicative list is: logistic regression, k-nearest neighbours, discriminant analysis, Gaussian processes, artificial neural networks; exact details may vary from year to year.

3. Unsupervised learning methods. An indicative list is: K-means clustering, dimension reduction such as principal component analysis; exact details may vary from year to year.

Learning outcomes

By the end of the module, students should be able to:

• Evaluate data analysis problems and select appropriate supervised and unsupervised learning algorithms.
• Critically evaluate the strengths and weaknesses of machine learning algorithms.
• Implement data analysis algorithms.
• Interpret the output of various algorithms when applied to data sets.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

• Demonstrate facility with rigorous data science methods.

• Evaluate, select and apply appropriate techniques to a variety of situations.

• Demonstrate knowledge of and facility with data science concepts, both explicitly and by applying them to the solution of problems.

• Create structured and coherent arguments communicating them in written form. 

• Construct logical mathematical arguments with clear identification of assumptions and conclusions.

• Reason critically, carefully, and logically.

Transferable skills

• Problem solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Know how to learn from failure.

• Self awareness: Reflect on learning, seeking feedback on and evaluating personal practices, strengths and opportunities for personal growth.

• Communication: Present arguments, knowledge and ideas, in a range of formats.

• Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Manage priorities and time. Self-motivated, setting and achieving goals, prioritising tasks.