CSMML16-Machine Learning

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Module version for: 2017/8

Email: x.hong@reading.ac.uk

Summary module description:

This module covers the topic of machine learning.

Aims:
The dramatic growth in practical applications for machine learning has been accompanied by many important developments in the underlying algorithms and techniques. This module will introduce the major concepts and algorithms in the field of machine learning.

Assessable learning outcomes:
By the end of the module students should be able to understand the main trends in machine learning and to describe principles of these algorithms.

Additional outcomes:

Outline content:

Vector calculus and Lagrange method, Gaussian distribution and Parzen window, the k-nearest neighbour and K-means clustering, mixture of Gaussians, probabilistic neural networks, linear discriminant, neural Networks, radial basis function neural, KKT condition, support vector machine, boosting.

Brief description of teaching and learning methods:
Lectures

Other information on summative assessment:

Formative assessment methods:

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

Length of examination:

2 hours.

Requirements for a pass:

50%.

Reassessment arrangements:
Exam in August/September.

Additional Costs (specified where applicable):

Last updated: 31 March 2017