Computational Social Intelligence (H) COMPSCI4080

  • Academic Session: 2019-20
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

The course introduces the core methodologies behind automatic approaches aimed at making sense of social and psychological aspects of human behaviour. In particular, the course shows 1) how to design and organise the observation of human behaviour in view of the application of automatic approaches, 2) how to apply psychometric instruments for the quantitative analysis of social and psychological phenomena, and 3) how to apply basic statistical techniques to human behaviour analysis and understanding. The course is interdisciplinary and it requires the acquisition of both computing and social psychological notions. The application areas to which the course is relevant include, e.g., social robotics, user experience analysis, social media analytics, surveillance and e-health (the list is not exhaustive).

Timetable

Three hours per week.

Requirements of Entry

It is recommended, but not mandatory, to attend "Artificial Intelligence", "Data Fundamentals" or "Machine Learning".

Excluded Courses

Computational Social Intelligence (M)

Co-requisites

None

Assessment

Examination 80%, Report 20%.

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? No

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

 

The report cannot be reassessed because it revolves around work that will be done during the 10 hours of practical work planned for the course. This means that the work at the core of the report cannot be done after the course and, hence, there is no possibility of improvement and consequent reassessment.

Course Aims

The aim of the course is to introduce the students to the main computational methodologies for automatic analysis of human behaviour. In particular the course teaches how to design and organise the observation of human behaviour in view of the application of computational approaches. Furthermore, it shows how to quantify social and psychological phenomena through the application of standard psychometric questionnaires. Finally, it introduces basic methodologies - based on machine learning and statistics - aimed at mapping behavioural observations into high-level interpretations of human behaviour that take into account social and psychological aspects of human-human and human-machine interactions.

Intended Learning Outcomes of Course

By the end of this course, the students will be able to:

1. Design and organise the collection of behavioural data in view of the application of statistical and computational methodologies for human behaviour understanding;

2. Measure social and psychological constructs - in quantitative terms - through the adoption of standard psychometric questionnaires; 

3. Apply basic statistical methodologies (e.g., k-Means and Naïve Bayes Classifier) to automatically map behavioural observations into social and psychological constructs.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.