Artificial Intelligence course curriculum

This unit develops the core programming and mathematical skills needed for AI. You will explore fundamental programming concepts including logic, linear algebra, calculus, and probability. You’ll apply these foundations to AI related computational tasks, developing your ability to design, build, test, and evaluate programmes and connect mathematical theory with practical AI applications.

You’ll learn to:

• Design, build, test, and evaluate computer programs
• Perform key mathematical operations used in AI, including elements of logic, calculus, linear algebra, probability, and statistics
• Create computational and mathematical problems based on AI scenarios
• Relate mathematical theory to practical programming tasks and AI applications

Topics covered in this unit may include (but are not limited to) the following:

• Fundamental programming concepts, including program structure, control flow, data types, and abstraction
• Problem analysis, algorithm design, implementation, and evaluation
• Tools and techniques for developing and evaluating programs, including development environments and testing frameworks
• Mathematical notation and formal reasoning
• Propositional and predicate logic

This unit explores the core concepts and methods used in AI and machine learning (ML). You will study how intelligent systems represent problems, reason under uncertainty, and learn from data, while applying algorithms to real-world scenarios through practical activities. You’ll develop and evaluate algorithmic solutions while exploring methods including search, probabilistic reasoning, Bayesian models, and more.

You’ll learn to:

• Apply core AI and ML concepts to analyse problems and select appropriate computational techniques  
• Design and implement algorithmic solutions in code for AI-related tasks, demonstrating accuracy and efficiency  
• Evaluate how models represent uncertainty and make predictions from data using probability and statistical reasoning  
• Compare core AI and ML methods, considering their assumptions, strengths, and limitations in practical contexts  
• Reflect on the ethical, societal, and legal considerations involved in developing and deploying intelligent systems

Topics covered in this unit may include (but are not limited to) the following:

• Goals, definitions, and paradigms of AI
• Problem-solving and search methods, including heuristic, adversarial, and nature-inspired approaches
• Constraint satisfaction and planning concepts
• Logical reasoning, including propositional and first-order logic
• Probabilistic reasoning and uncertainty, including Bayesian models and Markov processes

This unit explores how AI techniques are applied to real-world problems using data, software systems, and deployment practices. You will work with tools and frameworks for building, evaluating, and operating AI-enabled applications, while considering performance, scalability, and responsible use. You’ll also explore how machine learning models are integrated into larger software systems and applied in practical settings, including deployment, operation, and maintenance in production environments.

You’ll learn to:

• Select and apply appropriate AI techniques and tools to solve data-driven and real-world problems
• Design and implement applied AI solutions using relevant programming languages, software libraries, and data processing frameworks
• Analyse the factors that affect the performance and scalability of AI systems, including data quality, computational constraints, and system architecture
• Integrate ML models into larger software systems, considering deployment and maintenance in production environments
• Evaluate the suitability of data management, cloud, and large-scale computing technologies for applied AI scenarios

Topics covered in this unit may include (but are not limited to) the following:

• Applied AI use cases and data-driven problem formulation
• Programming tools and libraries for applied AI and data science
• Data ingestion, representation, storage, and processing pipelines
• ML workflows, from model development to evaluation
• Integration of AI components into larger software systems

This course aims to give students an understanding of current theoretical methodological and practical research issues around human interaction with robots and other computational intelligence. Students will gain relevant knowledge and skills related to the design, implementation, evaluation, and management of systems involving humans and intelligent machines.

This course will raise students' awareness of ethical and related challenges and constraints around the coexistence and collaboration of humans and intelligent machines. Participants will also gain experience in researching advanced topics in computer science, summarising the current state of the art, undertaking a relevant study, and presenting the results.

You’ll learn to:

• Demonstrate an understanding of current challenges in systems involving humans and intelligent machines
• Show awareness of intelligent systems design issues
• Critically evaluate examples of the design and deployment of intelligent systems
• Recognize and challenge advances in the state of the art of intelligent systems
• Design, conduct, and critique original research to address questions and challenges in the design and use of systems involving humans and machine intelligence

Topics covered in this unit may include (but are not limited to) the following:

• What is machine intelligence?
• A systems approach to human-machine interaction
• What aspects of humans and non-human agents should be considered in designing intelligent systems?
• Robots and diverse human needs, e.g. the young, the old, and people with disabilities

This unit introduces a wide range of NLP techniques and applications from the most basic to the advanced. By the end of the unit, students will be taught both theoretical knowledge and practical skills in NLP, learn about the fundamental concepts and most popular tasks and implementation strategies, and be able to structure their own NLP projects in an end-to-end manner.

You’ll learn to:

• Demonstrate knowledge of the fundamental principles of natural language processing
• Demonstrate understanding of key algorithms for natural language processing
• Write programs that process language
• Evaluate the performance of programs that process language
• Assess the feasibility and appropriateness novel NLP approaches presented in literature

Topics covered in this unit may include (but are not limited to) the following:

• An introduction to NLP systems
• Information retrieval
• Information extraction
• Text classification approaches
• Unsupervised approaches in NLP
• Sequence-based prediction and modelling in NLP
• Semantic tasks
• Current challenges and future directions

This unit provides a solid foundation in the exciting and fast-moving field of reinforcement learning. Reinforcement learning is concerned with training agents to select appropriate actions in their environments to achieve some goal. The types of problems tackled in reinforcement learning are very different from those tackled in other branches of machine learning. By the end of this unit, students should be able to identify sequential decision problems in the real world, formulate them as Markov decision processes, select appropriate solution methods, and implement them successfully.

In the first half of the unit, students will cover the fundamentals of reinforcement learning. Starting from the very basics, students will build up fundamental concepts from first principles, before looking at key reinforcement learning algorithms and applying them to solve simple problems. In the second half of the unit, students will apply these key ideas to more complex problems using function approximation. At the very end of the unit, students will study some active areas of research on the cutting-edge of the field.

You’ll learn to:

• Describe how reinforcement learning problems differ from supervised learning problems such as regression and classification
• Formulate real-world problems to demonstrate learning problems in context
• Critically evaluate a range of basic solution methods to reinforcement learning problems
• Analyse the difficulties encountered in solving large, complex reinforcement learning problems

Topics covered in this unit may include (but are not limited to) the following:

• The reinforcement learning problems
• Markov decision processes
• Dynamic programming methods
• Monte-Carlo methods

In this unit, the focus is on real-world context in which AI technologies are conceived, developed, and produced, as well as the effect that the implementation of these technologies has on our society, our economy, and our politics. We will use real-world use-cases of AI technologies to illuminate these themes and to see how theoretical notions play out in practice. Each case study looks at an application of AI Technologies which promises to revolutionise aspects of our society, but with the actual effects far from universally positive.

This unit introduces key frameworks of entrepreneurial thinking and activity, as well as key considerations and challenges for the entrepreneur and an introduction to fundamental business concepts (e.g. how to seek funding, key operational considerations etc.). Throughout this unit, there are a variety of examples of entrepreneurial activity. Some focus on technology start-ups, but others focus on other industries and sectors with examples chosen to highlight particular approaches, activities, thinking, and opportunities.

You’ll learn to:

• Identify and analyse market and entrepreneurial opportunities
• Develop business strategy to take advantage of that opportunity
• Critically consider key operational issues
• Investigate alternative funding and financial strategies
• Identify and address key IPR, legal, social, ethical, and professional issues
• Locate and use entrepreneurial resources
• Develop a business plan
• Reflect on the skills and behaviours of entrepreneurs
• Evaluate the phases and challenges of the entrepreneurial process
• Evaluate the resource challenges associated with entrepreneurship

Topics covered in this unit may include (but are not limited to) the following:

• History of entrepreneurial thought
• Market analysis, corporate structure, business funding, hiring, operations, marketing, risk management
• Legal, social, ethical, and professional issues
• Writing a business plan

Deep learning, a subfield of machine learning, has recently been revolutionising many fields, driving the recent artificial intelligent explosion. This unit is designed to provide a fundamental understanding of deep learning techniques and their various applications in many areas. You will explore a variety of deep learning models, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Through a hands-on approach, you will gain practical experience in implementing the above deep learning algorithms, including how to build, train, evaluate and fine-tune deep learning algorithms to solve practical problems. You will also explore many of the real-world applications of deep learning that can be found across various industries and research areas.

You’ll learn to:

• Demonstrate a basic understanding of the important theoretical concepts and algorithms in modern machine learning
• Demonstrate familiarity with state-of-the-art applications of machine learning and open research questions
• Appraise the suitability of various machine learning methods for a given application and write code in a relevant programming language to solve problems
• Demonstrate an understanding of a range of deep learning techniques, including MLP, CNN and RNN
• Demonstrate an understanding of how deep learning algorithms work, how to build them and how to train them
• Apply deep learning techniques to solve real-life problems using deep learning libraries

Deep learning, a subfield of machine learning, has recently been revolutionising many fields, driving the recent artificial intelligent explosion. This unit is designed to provide a comprehensive understanding of deep learning techniques, ranging from foundational knowledge to the latest advancements in the field. You will explore a variety of deep learning models, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), attention mechanisms, and Transformers.

Through a hands-on approach, you will gain practical experience in implementing the above deep learning algorithms, including how to build, train, evaluate and fine-tune deep learning algorithms to solve practical problems. You will also gain experience in training deep neural networks with Cloud GPU.

You’ll learn to:

• Analyse the foundational principles of deep learning and their theoretical underpinnings
• Design and implement popular deep learning architectures for various tasks
• Evaluate the performance of deep learning models through training, validation, and fine-tuning processes
• Apply deep learning techniques to solve real-world problems across diverse domains
• Critique recent advancements and research trends in deep learning to assess their relevance and impact
• Demonstrate proficiency in using cloud-based GPU platforms for training and deploying deep learning models

You’ll learn to:

• List the key components of a robotics application
• Explain the operating principles of various robot sensors, in particular of the intensity camera as well as the associated challenges in raw data acquisition
• Understand image as a data structure and apply basic processing and manipulation algorithms to it to achieve specified goals
• Describe and classify hardware options in data acquisition and locomotion tasks
• Discuss the interaction of hardware with interpretation and control algorithms
• Discuss the taxonomy and the operating principles of environment search algorithms, with the relative advantages and disadvantages of different variants
• Discuss the historical development of robotics, providing examples, and the current technical and non-technical challenges the field faces.
• Understand the reasons why technical challenges arise from the theoretical point of view
• Analyse operating principles of various approaches in different robot perception tasks such as data acquisition and interpretation and understand how these can be classified to build taxonomies (understand the methods compared to each other)

The Robotics and Machine Vision unit is a technical introduction to this application domain, with a particular focus on perception systems. This unit covers key aspects of the pipeline such as raw data acquisition, processing & representation and interpretation. The main emphasis will be on the algorithms, yet the unit also touches on some hardware aspects of robotics, such as those involved in sensing and locomotion.

With perception systems being the focus, and intensity camera the designated main sensor, the unit ventures into the domain of machine vision and graphics, covering important topics of scene reconstruction, modelling, and manipulation in 2D and 3D using projective geometry. Robotics as an area of research is given context, in terms of history of its development against the backdrop of generic AI evolution, as well as in terms of ethical practices, safety, and accountability. The unit also includes state-of-the-art robotics application examples where theory can be seen applied in the real world.

You’ll learn to:

• List the key tasks of a robotics application and the building blocks of a robotic perception system
• Explain key algorithmic paradigms involved in robot perception system (e.g., SLAM)
• Explain the operating principles of various robot sensors in particular of the intensity camera, as well the associated challenges in raw data acquisition
• Explain the core theory behind visual data interpretation algorithms, and apply the knowledge in practice
• Explain the core theory behind scene representation and manipulation in 2D and 3D, and apply the knowledge in practice
• Describe and classify hardware options in data acquisition and locomotion tasks
• Discuss the interaction of hardware with interpretation and control algorithms
• Discuss the historical development of robotics, providing examples, and the current technical and non-technical challenges the field faces

Undertake an individual research or developmental project that applies your knowledge and skills to a significant piece of work contributing to the field of artificial intelligence. You will develop an agreed project proposal, critically evaluate relevant academic literature, select appropriate research and technical methods, and carry out your project independently with guidance from an academic supervisor.

Depending on the nature of your project, your work may combine analytical, experimental, theoretical and software-based approaches. You will implement and evaluate your chosen approach, reflect critically on the outcomes and consider future directions, culminating in a comprehensive, professionally written dissertation.

The dissertation can be completed over 4, 8 or 12 months, giving you flexibility to choose a pathway that suits your circumstances. Students choosing a shorter pathway should expect a higher weekly workload.

You’ll learn to:

• Plan and complete an independent research project to a professional standard
• Find, critically evaluate and synthesise relevant artificial intelligence research
• Conduct independent research in line with appropriate ethical principles and processes
• Apply appropriate analytical, design, development and investigative methods to a technical AI problem
• Communicate your research, methods and findings clearly in a well-structured dissertation
• Critically evaluate your project and its outcomes, identifying limitations and opportunities for future work