Areas of Expertise
- Machine Learning
- Data Science
- Wearables (EEG)
- Applied Deep Learning
- Signal Processing
This module gives students an understanding of Monte Carlo methods, which use random simulation to computationally solve physical problems which may be difficult if not impossible to solve analytically. The delivery style of the module is very much tutorial in nature: only basic programming and probability theory will be taught and required, with the module concentrating on practical weekly lab-based problem-solving sessions.
This module will introduce students to some fundamental research methodologies and good practice in research. They will undertake background research including a literature review and specify the aims of their MSc project.
This module will take a look at how Machine Learning and Artificial Intelligence (black box) and data driven systems are applied in various real-world contexts. It will cover a variety of successful and unsuccessful case studies from academia and industry. Lectures will provide theoretical framework through which students will learn to construct their analysis and evaluate various forms of ML / AI algorithms for their appropriateness to be deployed in society as decision support systems. Students will be expected to read published literature and contribute to seminars by preparing presentations on new material and take part in reasoned scientific debate on contemporary ethical issues in the design and deployment of data and Intelligent systems.
This module gives an overview on theoretical and methodological debates contemporary Digital Economy and Society research with specific focus on Human Computer Interaction. This module helps students understand how human experience can influence the design and adoption of digital into services and the lives of individuals and communities.Students explore the advanced literature and research results underpinning the field. Understand, through a series of Classic papers, the practical application of qualitative and quantitative techniques for the study of soci-technical assemblages in a digital by default world, as well as recent work from the leading figures. Students achieve a clear view of the 'cutting edge' and issues in the field and where things are happening. The module is very interactive, and students will be expected to give presentations.
This module will discuss some of the most widely used and artificial intelligence and machine learning, regression & clustering techniques and their applications to big data social science questions. The students will gain and understanding of both strengths and weaknesses of learning and practical know-how in applying those theories to real world problems. Topics include big social data concepts, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.