Aims:

The module targets students who have no previous knowledge in cognitive science and emotion theory and therefore the aim of Part A of the module is to give a basic introduction to the theory of emotion from physiological and psychological viewpoints and to understand its importance in human decision and communication processes. Part B will concentrate on the application of machine learning techniques to automatic emotion recognition by looking at current applications (e.g. in entertainment, education, and health) and available sensing technology. Part C will focus on the challenges in designing robots that are capable of socially interacting with humans. Examples of current applications (e.g. in entertainment, health, rehabilitation, service robotics) will be used to identify problems and discuss affective computing challenges and approaches for the topics taught in Parts A and B.Learning outcomes:

On successful completion of the module, a student will be able to:

Have basic knowledge of emotion models and of how technology (e.g. robot) can be endowed with the ability to affectively and socially interact with its user.
	Understand the challenges that affective computing and social HRI pose to the machine learning field and identify the advantages and disadvantages of different approaches to address those issues.
Content:

Emotion theory:

What is affect, emotion, mood?;
	Why do we have emotions?;
	Neurological and psychological perspectives;
	How do humans express and recognise emotions?;
	Emotion expression models, appraisal theories;
	Affective and social interaction.
Affective computing:

Definition;
	Aims and current challenges.
	Applications; emotion recognition.
	How to select and use sensors for data collection.
	How to build an automatic emotion recognition system from: single modality, facial expressions, body expressions, touch expressions, voice, bio-signals and multimodal fusion.
Introduction to Physiological computing:

Key concepts (physiological sensing, affect recognition, biofeedback).
	How to build low-cost physiological computing systems (e.g. emergent wearable devices, low-cost cameras).
	Types of affect-related physiological signals and how to obtain features (cardiovascular, respiratory, perspiratory etc).
Human-Robot Interaction (HRI):

Social robotics: motivation and emotions in robot.
	Emotion based architecture.
	Ethical issues in Affective Computing and HRI research.
Requisites:

To be eligible to select this module as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) have some machine learning background, for example from Supervised Learning (COMP0078), Introduction to Machine Learning (COMP0088), and Introduction to Deep Learning (COMP0090); and (3) have some programming skills (for example, Python, MATLAB, Java, C, C++).

There is no imposed programming platform.