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: CSMMA16 Mathematics and Statistics
Modules excluded:
Current from: 2020/1

Email: lily.sun@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:

• Understand Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Ensemble methods


• Understand Deep Neural Networks, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN).


• Determine appropriate machine learning methods for supervised and unsupervised problems.


• Explain the process of training and making predictio ns 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.

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

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 examination paper in May/June.

Summative assessment- Coursework and in-class tests:

One project-based assignment.

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 2-hour examination paper in August/September. Note that the resit module mark will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (50% exam, 50% coursework).Ìý

Additional Costs (specified where applicable):

Last updated: 16 April 2020