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MATH6168 Machine Learning

Module Overview

The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets.

Aims and Objectives

Module Aims

The aims of the module are: to study general-purpose methods for supervised and unsupervised learning and apply them in practice using statistical software.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Understand and apply basic criteria that define successful supervised and unsupervised learning.
  • Understand the statistical underpinnings to various prediction and classification algorithms, and know when to apply different methods.
  • Apply appropriate methods to a variety of data sets and learning problems.
  • Understand the theoretical underpinnings of the discussed learning methods, and their implementation in appropriate statistical software.

Syllabus

- Introduction to supervised and unsupervised learning. - Mean squared error, bias/variance trade-off and cross-validation. - Prediction and regression, e.g. neural networks, Gaussian processes. - Classification, e.g. support vector machines. - Classification and regression trees: boosting and model-averaging. - Clustering. - Deep learning and hierarchical methods.

Learning and Teaching

Teaching and learning methods

36 Lectures and/or problem classes.

TypeHours
Independent Study114
Teaching36
Total study time150

Resources & Reading list

Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 

Arnold, T., Kane, M. and Lewis, B.W. (2019). A Computational Approach to Statistical Learning. 

James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. 

Assessment

Assessment Strategy

The assessment for the repeat candidates will be based completely on the final examination.

Summative

MethodPercentage contribution
Coursework 50%
Exam 50%

Referral

MethodPercentage contribution
Exam 100%

Repeat Information

Repeat type: Internal & External

Linked modules

pre-requisites: MATH2010 or STAT6083 or MATH6170

Costs

Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Course texts are provided by the library and there are no additional compulsory costs associated with the module.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.

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