COURSE CURRICULUM

The Artificial Intelligence MSc at Brunel includes a comprehensive range of compulsory modules designed to develop both theoretical understanding and practical skills across the AI and data science landscape. Students begin with quantitative data analysis, learning statistical methods such as regression, bivariate and multivariate analysis, and graphical models, while critically evaluating the strengths and limitations of these approaches. Data visualisation teaches students to present insights effectively through dashboards, infographics, and interactive narratives, enhancing communication to decision-makers. Research project management develops skills for designing and executing independent, scholarly research, while ethics and governance of digital systems fosters awareness of responsible use, regulation, and societal implications of intelligent systems. Core AI concepts are explored, enabling students to analyse problems, select appropriate methods, and evaluate outcomes. Deep learning covers neural network architectures, including convolutional, recurrent, generative, and reinforcement models, with a focus on practical application. Modern data introduces principles of data management, preparation, cleaning, and predictive analytics project planning. Machine learning develops analytic techniques to extract insight and value from heterogeneous datasets, including principal component analysis, cluster analysis, decision trees, random forests, and support vector machines, while emphasizing performance evaluation. Collectively, these modules equip students with a robust foundation in AI, data analysis, and ethical application, preparing them for both academic research and industry practice.