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: 2021/2

Module Convenor: Dr Yevgeniya Kovalchuk
Email: y.kovalchuk@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.

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.Ìý


• Explain the process of training and making pre dictions 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:

Classification and Clustering

• Support Vector Machines

Neural Networks:

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• Stochastic gradient descent


• Activation functions


• Feedforward and recurrent architectures


• Convolutional neural networks

< p>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:

Ìý	Autumn	Spring	Summer
Lectures		10
Practicals classes and workshops		6
Guided independent study:		84
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	100	0
Ìý	Ìý	Ìý	Ìý
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 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:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 􀀓Penalties for late submission for Postgraduate Flexible programmes􀀔, which can be found here: