This unit gives an in-depth overview of Machine Learning, exploring both unity and diversity among different ML paradigms and why this diversity is needed and how it can be exploited. The paradigms covered include: Introduction: tasks, models and features; Tree and Rule models; Linear and Distance-based models; Probabilistic models; Model ensembles; Deep learning. The unit will provide students with a solid analytical and practical framework for further work in data-driven AI.

This unit provides an introduction to knowledge-driven AI from the perspective of computational logic. It covers the basic principles of knowledge representation and automated inference by means of logic programming languages, which have pattern matching and backtracking search as primitive operations.

This then leads to more advanced methods in natural language processing and machine learning which exploit the representation and reasoning power of logic programming.

This unit presents the theoretical foundations for interactions that are narratively structured, reflecting the human bias towards organising our experiences as (language, logical and visual) narratives. Understanding the narrative structure requires a deep understanding of Natural Language Processing (NLP) components going from low-level text mining and segmentation to discourse-level analysis and summarisation. The unit builds upon these to teach core concepts in dialogue management, argumentation theory, and computational modelling of narratives.

This unit gives a solid grounding in fairness, accountability, transparency, privacy and trustworthiness in AI, and related concepts relating to ethics, law and regulation. Using case studies we will present and analyse these concepts from the perspective of industry, academia and government. Wherever possible these case studies will be drawn from PhD projects from earlier-cohort CDT students or other PhD students in the school.

This unit introduces CDT students to a range of research methods in interactive AI, and prepares them to carry out their own research in the Summer Project and subsequent PhD research. Through the unit students will improve the breadth and depth of their general AI knowledge, learn how to process and present scientific material, and how to plan for a larger research project.

Research seminars given by external and internal speakers will serve as exemplars of state-of-the-art AI research. Students will also research selected topics from the literature and present their findings in oral and written form (first deliverable). The topics will be chosen to be practically applicable and make students reflect about future research directions and about their own research. The second deliverable of the unit will be a Project Synopsis that prepares the ground for the Summer Project.

This unit introduces key data science concepts and their application to support data-driven approaches to problem solving. The aim of this unit is to allow students to acquire fundamental skills covering the full data science pipeline, including the pre-processing, manipulation, integration, storage, exploration, visualisation and privacy.

Students will study techniques to transform raw data into advanced representations that will enable a deeper understanding of the original data:

• Data ingress and pre-processing
• Data storage and data management
• Data transformation and integration
• Data exploration and visualisation
• Data sharing, privacy and anonymisation

The students will also gain practical skills in handling structured and unstructured data, gaining hands-on experience of software tools widely used in real-world settings.

This unit will explore the techniques and methodologies developed within Artificial Intelligence to represent and reason with information which is uncertain, imprecise or fuzzy. The unit will provide an overview of a range of different approaches explaining both the mathematics and the underlying philosophy and investigating practical applications.

Aims:

• To provide students with an overview of uncertainty modelling techniques and formalisms
• To provide students with an in-depth study of the mathematics and philosophy underlying these techniques
• To provide a detailed analysis of the application of uncertainty modelling in intelligent systems.