The first year involves a series of modules covering the five core foundations:
- Data-Driven AI
- Knowledge-Driven AI
- Human-AI Interaction
- Responsible AI
- Interactive AI in Context
You will complete a range of individual and group projects in preparation for your research project. The material is divided into three main teaching blocks - Teaching Block 1 (TB1, Autumn-Winter), Teaching Block 2 (TB2, Spring) and Teaching Block 3 (TB3, Summer).
Machine Learning Paradigms
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.
Computational Logic for Artificial Intelligence
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.
Dialogue and Narrative
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.
Advanced Topics in AI
This seminar-style unit introduces advanced and state-of-the-art topics in AI. There will be a mix of presentations by academics and students. The goal of the unit is to both improve the breadth and depth of general AI knowledge and to learn how to process and present scientific material.
The selected topics are chosen to be practically applicable and make students reflect about future research directions. Some topics might not strictly AI but related; they are included to understand the wider context of AI. Examples of topics to be covered in the first year include: Explainable and Interpretable AI; Reinforcement learning; Experimental design; Evaluation and psychometrics.
Applied Data Science
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.
Uncertainty Modelling for Intelligent Systems
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.
- 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.
Interactive AI Team Project
The aim of this unit is to provide students with practical experience in applying user-centred, theory-informed and evidence-based design and implementation methodologies to a real-world interactive AI challenge. The work will be done in teams of 3-5 students.
- Software engineering methods and tools for interactive AI pipelines
- Approaches to participatory and user-centred design
- Development of low and medium fidelity prototypes
- Approaches to evaluating interactive AI applications