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 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
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
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
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
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
Optional Units: General Pathway
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 software architectures for a mobile robot to popular robotic applications in a real-world scenario.
You’ll learn to:
- Explain and analyse the key elements of the design of a mobile robot;
- Describe and apply popular AI and machine-learning technologies for an intelligent mobile robot;
- Demonstrate typical locomotion for a mobile robot and understand the uncertainty in a real-world scenario;
- Apply scientific understandings to specific case examples of the application to intelligent mobile robots.
Optional Units: Technical Pathway
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 knowledge and awareness of key concepts in intelligent mobile robotics such as the development of typical visual-based robot perception for mobile robots including visual sensors and geometry estimation in a real-world scenario.
You’ll learn to:
- Explain and analyse the key elements of the design of a mobile robot;
- Describe and apply popular AI technologies for an intelligent mobile robot;
- Demonstrate machine vision technologies for visual-based robot perception and solve challenges in a real-world scenario;
- Apply scientific understandings to specific case examples of the applications to intelligent mobile robots.
All right let's get back to the main page. So the first thing is that you'll see in each unit is this welcome page. (Let's see if I can mute the…it's muted great stuff!) So in the welcome page you will have a video which is an introduction to the course itself. Under here you'll have lots of resources that you can go to. We'll have a unit overview – so that will be information about what we're trying to teach in this particular unit. And then we also have your learning outcomes.
So let's say these specific ones are sort of the ones that you would go and have a look at as you start each unit. The following resources I would say you probably end up coming back to multiple times throughout your studies. And those are for example greater tests and assignments. This is a really great one it gives you an overview of exactly when your graded tests are due. Obviously these are dummy dates. But exactly when your graded tests would do anything that you need to do in order to succeed in this particular unit. So you've got your due dates in here. We want to make sure that we present this to you for every course for every unit that you take, because it's with the understanding of course that this is this is a part-time degree and therefore you everyone is busy. We're all busy people and so this is a chance to sort of have a look in advance and see when is it when am I going to be working a little bit harder perhaps because I’m working towards a graded assignment.
Let's look at an example week. So each week that you are doing so each course that you do each unit that you do will be eight weeks. So you will have eight of these pages for each unit at the top you've got an introductory video of taking the sound off you got an introductory video to that week where we talk you through what kind of things you might see in that particular week. And then what we've done here is we've broken down each page into sort of bite-sized chunks. So rather than just giving lots and lots of information we've broken everything into lessons. You'll see the different styles of lessons. So if you see a lesson like this it might have a little bit less information. Whereas one like this basically means an accordion so there's a little bit more information than those lessons.
Dr Ben Ralph
Dr Ben Ralph is the Director of Studies for the Artificial Intelligence online MSc, and teaches on both the Artificial Intelligence and Computer Science online MSc. As a researcher Ben has mainly studied structural proof theory: in particular the problem of proof identity, using techniques including combinatorial proofs and Deep Inference, and before his PhD at Bath completed a Masters degree in Mathematics and Philosophy at the University of Oxford. Ben has also signed the pledge for sustainable research in theoretical Computer Science.