CSMML16-Machine Learning

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Email: t.thorne@reading.ac.uk

Type of module:

Summary module description:

This module covers the topic of machine learning.

Aims:

The aim of the module is to introduce students to current methods in machine learning and their application to real world problems.

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Assessable learning outcomes:

Students will be able to:

• Explain support vector machines and k-means methods


• Determine appropriate machine learning methods for clustering, classification and regression problems.


• Apply machine learning methods to perform clustering, classification and regression.


• Explain the process of training and making predictions with a neural network.


• Determine the appropriate neural network architecture for a particular problem.


• Apply multiple classes of neural network to real world problems involving image and text data.


• Understand ensemble methods in machine learning.


• Apply ensemble methods to real world data.

Additional outcomes:

Students will gain familiarity with machine learning and neural network libraries, and the Python programming language.

Outline content:

The module covers foundational topics in relevant machine learning algorithms:

Support Vector Machines

K-means clustering

Neural networks:

• Backpropagation


• Stochastic gradient descent


• Activation functions


• Feedforward and recurrent architectures


• Convolutional neural networks


• Generative adversarial networks


• Capsule networks

Ensemble methods:

• Boosting


• Bagging


• Stacking

Students will learn how to apply these methods in various domains using the Python language and libraries, including:

• Image classification


• Image synthesis


• Natural language processing

Brief description of teaching and learning methods:

The module consists of 10 lectures and weekly guided practical classes that implement methods covered in the lectures.

Summative assessment- Examinations:

One 1.5 hour exam.

Summative assessment- Coursework and in-class tests:

One project based assignment, due week 11 of Spring term.

Formative assessment methods:

Feedback in practical classes.

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

A mark of 50% overall.

Reassessment arrangements:

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Additional Costs (specified where applicable):

Last updated: 10 April 2019