CSMML16-Machine Learning

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

Module Convenor: Dr Tom Thorne

Email: t.thorne@reading.ac.uk

Type of module:

Summary module description:

This module covers the topic of machine learning.


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


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.

Contact hours:
  Autumn Spring Summer
Lectures 10
Practicals classes and workshops 10
Guided independent study: 80
Total hours by term 100
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Written exam 50
Project output other than dissertation 50

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:

  One examination paper of 2 hours duration in August/September.

Additional Costs (specified where applicable):

Last updated: 10 April 2019


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