Artificial Intelligence course structure

The Artificial Intelligence course curriculum is made up of 13 units, totalling 180 credits. You will be invited to select either the technical or the general pathway, each containing 3 units, totalling 30 credits. Each unit is centred on the application and practice of AI in a real world setting.

The course is delivered in four phases, the first covering the core principles of AI, programming and mathematics. Phase 2 explores the application of these principles in practice.

In phase 3, you will be asked to select from one of two pathways:

  • Technical: specialising in the technical side of AI, this route has a greater emphasis on programming and machine learning.
  • General: exploring AI in context, this pathway offers a comprehensive introduction to the wider subject area.

Over a minimum period of two years and three months, you must successfully complete all the units, including a research-based dissertation (phase 4). The units, which cost £722* per 10 credits, last eight weeks and run consecutively. Over the year, there are three short breaks, in December, April and August.

The course is delivered remotely and entirely online. Learning material is made available to students via a Virtual Learning Environment (VLE). Assessments are conducted online, also through the VLE. Students study one unit at a time, and learning support is provided through electronic discussion forums and live tutorial sessions.

Units

Introducing you to the core concepts of programming and the practical skills involved in producing programs to solve real-world problems, this unit will help build your confidence around a variety of programming languages and paradigms.

You’ll learn to:

  • Explain debugging and testing methods and how they contribute to robust coding
  • Design, evaluate and analyse the efficiency of data structures and algorithms
  • Implement advanced concepts of artificial intelligence programming using appropriate libraries and frameworks

By providing an understanding of the basic theory of probability and statistics, you’ll learn to recognise where and how theory can be applied in practice.

You’ll learn to:

  • Perform elementary mathematical operations in probability and statistics
  • Translate real-world problems into a probabilistic or statistical framework
  • Solve statistical problems in abstract form and critically interpret outcomes in a real-world context

Learn about the fundamental concepts of AI, including goals, foundations, and history of artificial intelligence, problem solving, logical and probabilistic reasoning, machine learning, state-of-the-art applications, and the legal and ethical implications.

You’ll learn to:

  • Understand a range of artificial intelligence techniques, their strengths and limitations
  • Learn the fundamentals of probability theory and its role in artificial intelligence
  • Apply various artificial intelligence techniques to simple problems

Develop the ability to contrast, analyse and solve problems using appropriate software and software development paradigms.

You’ll learn to:

  • Evaluate and contrast contemporary software engineering paradigms for software engineering issues
  • Compare and contrast the roles, responsibilities, benefits and drawbacks of different software development team structures
  • Identify the social, legal and ethical issues in the application of AI

Learn about the theory and practice of natural language processing through techniques including language models, named entity recognition, information extraction, text classification and speech recognition.

You’ll learn to:

  • Understand the fundamental principles and key algorithms of natural language processing
  • Write programs to process language
  • Evaluate the performance of programs that process language

Designed to build your confidence in numeric programming, this module explores how to undertake lower-level data science, using a language and its associated libraries, and how to scale it up to solve higher-level AI problems.

You’ll learn to:

  • Evaluate the features of different AI programming languages and software packages, focusing on data science
  • Apply a range of complex analytic methodologies, notably machine learning techniques
  • Assess the relevance of key ‘big data’ software technologies in different scenarios
  • Explore factors affecting complexity, performance, numerics, scalability and solution deliverability
  • Implement low-level data science functionality using a relevant programming language

Exploring current issues around human interaction with computational intelligence, you’ll understand how to design, implement, evaluate and manage systems involving humans and intelligent machines. You’ll also become more aware of ethical challenges around the coexistence of humans and intelligent machines.

You’ll learn to:

  • Adopt current practice and system developments involving humans and intelligent machines
  • Critically evaluate examples of the design and deployment of intelligent systems
  • Design, conduct and critique original research in the design and use of human and machine intelligence systems
  • Explore and challenge advances in the design of intelligent systems

Establish a practical understanding of intelligence and computation to solve problems within various strategies and approaches.

You’ll learn to:

  • Understand the advantages and disadvantages of a range of AI techniques
  • Appreciate AI as a practical mechanism to deal with computationally tough issues
  • Understand and implement AI concepts and create programs for AI
  • Critically evaluate state-of-the-art AI applications

By introducing you to the latest research in AI, you’ll be able to position your studies in a wider context, helping to guide your choice of dissertation project.

You’ll learn to:

  • Summarise and critique computer science research papers on AI
  • Distinguish various research themes in AI and highlight the research aims within each
  • Determine which research area you would like to work on for your dissertation
  • Critically analyse and review previous work in a chosen subject area
  • Undertake and document a detailed literature review in a chosen area of computer science research

Drawing on research from the previous unit, you’ll analyse various problem solutions and choose appropriate methods and approaches to implement your chosen route. In most cases your project will be a synthesis of both analytical and computational approaches to a significant, contemporary artificial intelligence problem.

You’ll learn to:

  • Identify the tasks to be completed and plan a scheme of work
  • Assemble and create the necessary analysis, design and development tools
  • Complete a well-structured and coherently written dissertation, including a discussion of the research outcomes of the work and future directions

Providing an overview of the theory and practice of machine learning, this unit explores central concepts and algorithms of supervised, unsupervised and reinforcement learning.

You’ll learn to:

  • Distinguish between different formulations of supervised and reinforcement machine learning
  • Understand the strengths and limitations of a wide range of machine learning techniques
  • Write code in a relevant programming language and employ software libraries to solve problems in machine learning

This module looks at the theoretical and political debates around the development, emergence and adoption of technologies in social and political practice, explaining the position of AI in these debates and the ways in which it can challenge political, social and economic relationships.

You’ll learn to:

  • Understand the debates around the development, emergence and adoption of digital technologies
  • Develop a critical understanding of the unique implications for artificial intelligence in political, economic and social settings
  • Appreciate how the above implications are reflected in, or challenged by, the adoption of artificial intelligence, machine learning and robotics

Providing knowledge and awareness of key concepts in intelligent mobile robotics, from control theory and PID controllers to robotic kinematics and robotic perception.

You’ll learn to:

  • Explain the typical design of mobile robots
  • Describe multi-sensory-based perception in real-world scenarios
  • Describe typical locomotion for mobile robots and their uncertainty in motion
  • Evaluate and apply machine learning methods for mobile robots

Offering a foundation for the theory and practice of machine learning, this module supports mathematical, statistical and computational skills to help you understand and implement contemporary machine-learning methods.

You’ll learn to:

  • Understand the important theoretical concepts and algorithms in modern machine learning
  • Recognise state-of-the-art applications of machine learning
  • Appraise the suitability of various machine learning methods for a given application

Learn the key issues around reinforcement learning and the basic solutions, while developing your understanding around algorithmic thinking for sequential decision making under uncertainty.

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

Gain an in-depth knowledge of practical artificial intelligence to control real-time autonomous systems, including autonomous robots, scientific simulations, and virtual-reality characters.

You’ll learn to:

  • Explain the typical design of mobile robots, then evaluate and apply machine learning methods
  • Describe multi-sensory-based perception in real-world scenarios
  • Understand typical locomotion for mobile robots and their uncertainty in motion
  • Evaluate and apply machine learning methods for mobile robots
* Valid up to and including September 2020 intake. Tuition fees are liable to increase each January. You should budget for an increase of up to a maximum of 5% each year.

Christina Keating

Dr Keating is a lecturer at the Department of Computer Science at the University of Bath. Passionate about Human Computer Interaction, her research is centred on the cognitive abilities of technology users, dual task paradigms and notification design. Recent research explored the impact of the process of idea generation and the modelling of performance of creative tasks on the design of ‘ideation’ support systems.