The main goal of this module is to explore the development and optimisation of intelligent, autonomous agents capable of outperforming human capabilities in various tasks. You'll learn the core concepts of intelligent agents, from fundamental AI paradigms like rule-based systems, planning, and learning, to advanced decision-making algorithms. The module emphasises both classical and modern AI techniques, showing how traditional ideas continue to inspire powerful innovations. Through practical exercises, you'll design, implement, and validate AI algorithms, enhancing your skills in problem-solving, critical thinking, and translating complex algorithms into functional code.

The main goal of this module is to equip students with the expertise to design and implement robust technology platforms essential for effective AI and Data Science systems. You’ll explore a range of technologies like Hadoop, Spark, and PyTorch Distributed, learning how to select, configure, and optimise them for large-scale, high-performance computing. The module focuses on principles of system architecture, distributed machine learning, and scalability, with real-world case studies and industry insights. By the end, you'll be able to architect and engineer data-driven systems, critically evaluate enterprise-scale IT solutions, and implement distributed machine learning models effectively.

This module aims to equip students with a deep understanding of the rapidly evolving field of Artificial Intelligence (AI), focusing on both cutting-edge methods and applications. You'll explore AI's role in areas like cybersecurity, ethical considerations, human-AI interaction, and emerging technologies like Quantum AI. The module prepares you to apply AI to real-world challenges or pursue research in innovative AI techniques, ensuring alignment with current industry and academic trends. Additionally, you'll develop skills in implementing AI solutions, making ethical decisions, and effectively communicating your findings in professional settings.

The main goal of this module is to provide students with cutting-edge knowledge in natural language processing (NLP) as applied in both industry and research. You'll learn how to collect, clean, and analyse language data at scale, using methods ranging from rule-based to deep learning techniques. The module covers key applications like machine translation, sentiment analysis, and summarisation, alongside discussions on language models, ethics, and bias in NLP. By the end, you'll be able to create scalable solutions for language data challenges, understand current NLP research trends, and enhance your skills in independent study, critical thinking, and effective communication.

Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.

The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision-making in business and other organisations.

The module aims to teach the elements of time series and econometric forecasting in such a way that passing students will be able to prepare technical and non-technical reports for clients based on these methods which are methodologically competent, understandable and concisely presented.

On successful completion of the module students should be able to:

The module introduces time series and causal forecasting methods so that passing students will be able to prepare methodologically competent, understandable and concisely presented reports for clients. By the end of the course you should be able to model causal and time series models, assess their accuracy and robustness and apply them in a real world problem domain.

The course provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. The course covers then entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the course is classification. The course content covers:

1. Exploratory data analysis and visualisation including dimensionality reduction methods like principal component analysis and multi-dimensional scaling
2. Classification methods like: k-Nearest Neighbours, Naive Bayes, Logistic Regression, Decision trees (Random forests), and Artificial Neural Networks
3. Performance Assessment and Model Selection

The course uses the R programming language and more specifically the RStudio integrated programming environment. The course makes extensive use of online video lectures from top scientists in the field, and (I hope) will be supported by DataCamp (I am currently in the process of enrolling students to DataCamp for the classroom to allow them free access to a large number of otherwise non-free DataCamp courses).

Curriculum Design: Select Bibliography

1. G. James, D. Witten, T. Hastie and R. Tibshirani (2013) An Introduction to Statistical Learning: with
Applications in R, Springer
2. M.R. Berthold, C. Borgelt, F. Höppner and F. Klawonn (2010) Guide to Intelligent Data Analysis,
Springer
3. P.-N. Tan, M. Steinbach and V. Kumar (2005). Introduction to data mining. Boston, Pearson Addison Wesley.

The purpose of this course is to understand and use mathematical models in making strategic, tactical,
and operational logistics decisions. Emerging logistical concepts will be introduced and the associated
mathematical modelling needs will be discussed. Algebraic formulations will be used as vehicle for
describing models and discussing their relationships. There will be a focus on modelling, the use of
professional software, and the understanding of results. For problems where exact solutions are hard to
achieve even for simple instances of the problem, heuristics will be discussed.

Depending on students need and level of programming skill, the computer workshops will focus on either
solver languages (e.g. GAMS, AMPL, MPL) and/or programming interfaces (PYOMO, CPLEX Concert,
Gurobi Python Interface).

This module introduces students to the fundamental principles of Geographical Information Systems (GIS) and Remote Sensing (RS) and shows how these complementary technologies may be used to capture/derive, manipulate, integrate, analyse and display different forms of spatially-referenced environmental data. The module is highly vocational with theory-based lectures complemented by hands-on practical sessions using state-of-the-art software (ArcGIS & ERDAS Imagine).

In addition to the subject-specific aims, the module provides students with a range of generic skills to synthesise geographical data, develop suitable approaches to problem-solving, undertake independent learning (including time management) and present the results of the analysis in novel graphical formats.

This module will immerse students in advances in ecological research and conservation that provide key skills for working as an ecologist in the era of Big Data. Teaching is delivered by world-leading researchers who are experts in biodiversity from coral reefs to tropical forests and freshwater lakes, ensuring a deep understanding of how data science can generate actionable insights for global conservation. Throughout the course, students will gain an understanding of the principles behind data science tools and techniques at the forefront of developing both a fundamental understanding of the natural world and urgent solutions to the global biodiversity crisis. The curriculum is dynamic and will adapt annually to address contemporary issues.

Indicative topics include:

1. Why do we need data science for biodiversity?

2. Big Data: advantages, challenges and solutions

3. Automating species ID for citizen science (AI, machine learning)

4. Tracking animal movements underwater (acoustic telemetry)

5. Quantifying 3D habitat structure (photogrammetry)

6. Biodiversity soundscapes in a noisy world (bioacoustics)

7. The ecological role of colour (machine learning)

8. Scaling up: from animal behaviour at global species distributions (geospatial)

9. Extended reality for ocean empathy (XR)

10. Responsible data science for biodiversity

Workshops offer in-depth exploration of advanced topics, such as AI’s role in predictive ecology, cutting-edge ecological technologies, biodiversity beyond species richness, data visualization strategies, and innovative data-driven solutions to the biodiversity crisis. Our interdisciplinary approach blends ecological and computational perspectives, preparing you for in-demand roles in the evolving ecology sector.

This module will equip the student with the understanding and skills required to use statistical methods to solve current ecological challenges in a robust and well-considered manner, translating statistical uncertainty into decision-making processes. These skills are highly sought after by conservation charities and non-governmental organisations.

Over the course of the module, the student will become familiar with the principles of statistical inference, including likelihood theory and Bayesian inference, and by the end of the module, they will be confident in justifying the use of one approach over the other and comparing and contrasting the results from the two methods. Students will experiment with different ecological data types and examine these through the lens of different visualisation and descriptive analyses.

Introducing epidemiology, the study of the distribution and determents of disease in human population, this module presents its main principles and statistical methods. The module addresses the fundamental measures of disease, such as incidence, prevalence, risk and rates, including indices of morbidity and mortality.

Students will also develop awareness in epidemiologic study design, such as ecological studies, surveys, and cohort and case-control studies, in addition to diagnostic test studies. Epidemiological concepts will be addressed, such as bias and confounding, matching and stratification, and the module will also address calculation of rates, standardisation and adjustment, as well as issues in screening.

This module provides students with a historical and general overview of epidemiology and related strategies for study design, and should enable students to conduct appropriate methods of analysis for rates and risk of disease. Students will develop skills in critical appraisal of the literature and, in completing this module, will have developed an appreciation for epidemiology and an ability to describe the key statistical issues in the design of ecological studies, surveys, case-control studies, cohort studies and RCT, whilst recognising their advantages and disadvantages.

This module addresses a range of topics relating to survival data; censoring, hazard functions, Kaplan-Meier plots, parametric models and likelihood construction will be discussed in detail. Students will engage with the Cox proportional hazard model, partial likelihood, Nelson-Aalen estimation and survival time prediction and will also focus on counting processes, diagnostic methods, and frailty models and effects.

The module provides an understanding of the unique features and statistical challenges surrounding the analysis of survival avant history data, in addition to an understanding of how non-parametric methods can aid in the identification of modelling strategies for time-to-event data, and recognition of the range and scope of survival techniques that can be implemented within standard statistical software.

General skills will be developed, including the ability to express scientific problems in a mathematical language, improvement of scientific writing skills, and an enhanced range of computing skills related to the manipulation on analysis of data.

On successful completion of this module, students will be able to apply a range of appropriate statistical techniques to survival and event history data using statistical software, to accurately interpret the output of statistical analyses using survival models, fitted using standard software, and the ability to construct and manipulate likelihood functions from parametric models for censored data. Students will also gain observation skills, such as the ability to identify when particular models are appropriate, through the application of diagnostic checks and model building strategies.

Clinical trials are planned experiments on human beings designed to assess the relative benefits of one or more forms of treatment. For instance, we might be interested in studying whether aspirin reduces the incidence of pregnancy-induced hypertension, or we may wish to assess whether a new immunosuppressive drug improves the survival rate of transplant recipients.

This module combines the study of technical methodology with a discussion of more general research issues, beginning with a discussion of the relative advantages and disadvantages of different types of medical studies. The module will provide a definition and estimation of treatment effects. Furthermore, cross-over trials, issues of sample size determination, and equivalence trials are covered. There is an introduction to flexible trial designs that allow a sample size re-estimation during the ongoing trial. Finally, other relevant topics such as meta-analysis and accommodating confounding at the design stage are briefly discussed.

Students will gain knowledge of the basic elements of clinical trials. They will develop the ability to recognise and use principles of good study design, and will also be able to analyse and interpret study results to make correct scientific inferences.

This module offers students an advanced understanding of statistics, and will explore the idea of missingness as a stochastic process. Students will develop and apply their knowledge in missing data formulas, focusing on the imputation model and the model of interest. More naive methods will be introduced, such as single imputation and list wise deletion, and students will develop the ability to recognise the limitations of each method and gain the knowledge required to identify situations where their use may be appropriate.

A portion of the module will introduce VIM software and explore its uses for finding missingness patterns.

This module will enhance deduction skills, and students will become accustomed to the differences between sampling and parameter uncertainty, in addition to noticing similarities between the Bayesian and imputation approaches.

Introducing epidemiology, the study of the distribution and determents of disease in human population, this module presents its main principles and statistical methods. The module addresses the fundamental measures of disease, such as indicence, prevalence, risk and rates, including indices of morbidity and mortality.

Students will also develop awareness in epidemiologic study design, such as ecological studies, surveys, and cohort and case-control studies, in addition to diagnostic test studies. Epidemiological concepts will be addressed, such as bias and confounding, matching and stratification, and the module will also address calculation of rates, standardisation and adjustment, as well as issues in screening.

This module provides students with a historical and general overview of epidemiology and related strategies for study design, and should enable students to conduct appropriate methods of analysis for rates and risk of disease. Students will develop skills in critical appraisal of the literature and, in completing this module, will have developed an appreciation for epidemiology and an ability to describe the key statistical issues in the design of ecological studies, surveys, case-control studies, cohort studies and RCT, whilst recognising their advantages and disadvantages.

Hierarchical data arise in a multitude of settings, specifically whenever a sample is grouped (or clustered) according to one or more factors with each factor having many levels. For instance, school pupils may be grouped by teacher, school and local education authority. There is a hierarchical structure to this grouping since schools are grouped within local education authority and teachers are grouped within schools. If multiple measurements of a response variable, say test score, are made for each pupil across multiple measurement times, the data are also longitudinal. This module motivates the need for statistical methodology to account for these kinds of hierarchical structure. The differences between marginal and conditional models, and the advantages and disadvantages of each, will be discussed. Linear mixed effects models (LMMs) for general multi-level data will be introduced as an extension to the linear regression model. Longitudinal data will be introduced as a special case of hierarchical data motivating the need for temporal dependence structures to be incorporated within LMMs. Finally, the drawbacks of LMMs will be used to motivate generalised linear mixed effects models (GLMMs), with the former a special case of the latter. GLMMs broaden the scope of data sets which can be analysed using mixed-effects models to incorporate all common types of response variable. All modelling will be carried out using the statistical software package R.

The course is designed to provide foundational knowledge in linear and non-linear time-series analysis through building awareness of various well used time-series models. While the module focuses on univariate analysis, students will have time to read around lecture notes and materials to extend their understanding of these methods. By the end of the course, the student should understand both the theoretical and practical foundations of time-series analysis, how to fit, and choose from a range of models. They will understand different methods of evaluating time-series model performance and how these models can be used to provide forecasts.