COLLECTIVE INTELLIGENCE - 2017/8

Module code: COM3024

Module provider

Computer Science

Module Leader

TANG HL Dr (Computer Sci)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 6

JACs code

I400

Module cap (Maximum number of students)

N/A

Module Availability

Semester 2

Overall student workload

Assessment pattern

Assessment type Unit of assessment Weighting
Practical based assessment PRACTICAL ASSIGNMENTS ON KEY TOPICS 20%
Coursework DEVELOPMENT OF NEW APPLICATIONS/FUNCTIONS 40%
Examination 2 HR CLOSED BOOK UNSEEN EXAMINATION 40%

Alternative Assessment

N/A

Prerequisites / Co-requisites

Learning outcomes

Attributes Developed
Understand various machine-learning algorithms and statistical methods for processing and interpreting the data from the Internet.   KC
Demonstrate adequate skills in developing applications and implementing functions using the algorithms discussed in the module. PT
Critically evaluate existing collective intelligence methods within the context of current trends. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Methods of Teaching / Learning

Assessment Strategy

Reading list

Reading list for COLLECTIVE INTELLIGENCE : http://aspire.surrey.ac.uk/modules/com3024

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.