University of St Andrews

Key information

SCOTCAT credits : 15

ECTS credits : 7

Level : SCQF level 11

Semester: 2

Availability restrictions: There are 80 spaces available on this module. If necessary, a ballot will be held to select students for the module.

Planned timetable: To be arranged.

Machine Learning enables computers to improve automatically with experience. A growing number of algorithms are being used to predict outcomes using patterns in collected data. This module covers the essential theory and algorithms, including mathematical foundations, and methodological approaches. It covers a variety of regression, classification and unsupervised approaches. It consists of lectures, and practical components with unassessed exercises and assessed practical coursework assignments with a final exam.

Relationship to other modules

Pre-requisite(s): Postgraduate - PGT: Before taking this module you must pass CS5001 and have achieved a Grade of B or higher in Higher or A-level Maths

Anti-requisite(s): You cannot take this module if you take ID5059

Learning and teaching methods and delivery

Weekly contact: 2 lectures (x 11 weeks), 1 lab session (x 5 weeks).

Scheduled learning hours: 27

Guided independent study hours: 127

Assessment pattern

As used by St Andrews: 2-hour Written Examination = 60%, Coursework = 40%

As defined by QAA
Written examinations : 60%
Practical examinations : 0%
Coursework: 40%

Re-assessment: 2-hour Written Examination = 60%, Existing Coursework = 40%

Personnel

Module teaching staff: TBC Module coordinator(s): Director of Postgraduate Teaching - Computer Science (dopgt-cs@st-andrews.ac.uk)