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: 2022/3

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 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.

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:

• µþ²¹³¦°ì±è°ù´Ç±è²¹²µ²¹³Ù¾±´Ç²ÔÌý


• Stochastic gradient descent


• Activation functions


• Feedforward and recurrent architectures


• Convolutional neural 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:

Ìý	Autumn	Spring	Summer
Lectures		10
Practicals classes and workshops		10
Guided independent study:		80
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
Set exercise	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: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

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):

1) Required text books:ÌýÌý

2) Specialist equipment or materials:ÌýÌý

3) Specialist clothing, footwear or headgear:ÌýÌý

4) Printing and binding:ÌýÌý

5) Computers and devices with a particular specification:ÌýÌý

6) Travel, accommodation and subsistence:ÌýÌý

Last updated: 22 September 2022