Artificial Intelligence Concepts: Practical Applications (Online)


artificial intelligence, n.

The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this.

source: Oxford English Dictionary

Artificial Intelligence (AI) has become ingrained in the fabric of our society, often in seamless and pervasive ways that may escape our attention day-to-day. The ability of machines to sense, process information, make decisions and learn from experience is a transformative tool for organisations, from governments to big business. However, these technologies pose challenges, including social and ethical dilemmas.

This course focuses on real-world applications of AI to significant problems facing the 21st century, covering critical concepts like AI ethics and fairness, with examples from disaster planning, sustainable development, and human health. By focusing on diverse case studies, it helps develop a critical approach to AI applications, a recognition of practical and ethical challenges, a strategy to keep abreast of developments in AI, and an ability to generalise knowledge to new domains. It is aimed at a general audience, including professionals whose work brings them into contact with AI and those with no prior knowledge of AI.

This is part of a series of courses that aim to confer an appreciation of how AI has already transformed our world, explain the fundamental concepts and workings of AI, and equip us with a better understanding of how AI will shape our society so that we can converse fluently in the language of the future.

This course does not involve any coding and instead focuses on concepts in Artificial Intelligence for a general audience.

Programme details

Unit 1: Introduction to AI concepts: practical applications

  • What is artificial intelligence?
  • Types of machine learning
  • The Business Process Model and Notation: modelling business processes


Unit 2: Ethical concerns raised by AI

  • The role of ethics in the development of AI and machine learning
  • Different ways of operationalising fairness in the context of AI
  • Ethical accountability for systems that learn and adapt
  • Transparency and AI systems


Unit 3: Replication, reproducibility and reuse in AI

  • Problems posed by replication, reproducibility and reusability of digital artefacts
  • The FAIR Guiding Principles: Findability, Accessibility, Interoperability, and Reusability
  • Applying FAIR to the reuse of digital artefacts relating to AI and ML


Unit 4: Staying abreast of AI developments

  • The importance of staying up to date with AI
  • Identifying key industry and research organisations and people
  • Key resources for keeping abreast of AI developments
  • Analysing popular articles and technical papers about AI


Unit 5: AI and the Sustainable Development Goals

  • The UN SDGs: Sustainable Development Goals
  • Applying AI to address the SDGs
  • The positive and negative impact of AI on the SDGs


Unit 6: Case study – Transfer learning for predicting poverty

  • Data as the new oil
  • Administrative data for public policy: identifying poverty lines and economic output
  • Exploiting multiple sources for prediction in complex environments
  • Harnessing Transfer Learning, Regression and Deep Learning


Unit 7: Case study – Social media for disaster management

  • The Sendai Framework for prioritising targets in disaster resilience
  • Monitoring disaster risk with GIS: Geographic Information Systems
  • The role of social networks, satellites and UAVs: unmanned aerial vehicles
  • Applications of Natural Language Processing and Latent Dirichlet Allocation


Unit 8: AI for fighting epidemics

  • Challenges for AI posed by epidemics and pandemics
  • Existing tools and frameworks used by organisations and nations
  • Applying AI to enhance existing frameworks for fighting epidemics


Unit 9: Case study – Contributions of AI towards developing vaccines

  • Proteins and vaccines: 3D molecular identification of vaccine targets
  • Cracking the problem of protein folding with deep learning
  • Enhanced prediction using Neural Networks and Gradient Descent


Unit 10: Case study – AI for predicting clinical deterioration

  • National Early Warning Scores: early detection in Intensive Care Units
  • Assimilating continuous and discrete vital signs for continuous monitoring
  • Retrospective analysis of risk factors from Electronic Health Records
  • Employing Gradient Boosting Models and Sequential Deep Neural Networks


Credit Application Transfer Scheme (CATS) points 

To earn credit (CATS points) for your course you will need to register and pay an additional £30 fee for each course you enrol on. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register when you enrol, you have up until the course start date to register and pay the £30 fee. 

See more information on CATS point

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education, you need to indicate this on the enrolment form but there is no additional registration fee. 


Digital credentials

All students who pass their final assignment, whether registered for credit or not, will be eligible for a digital Certificate of Completion. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so. 

Please note that assignments are not graded but are marked either pass or fail. 


Description Costs
Course Fee £385.00
Take this course for CATS points £30.00


If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:


Concessionary fees for short courses


Ms Judith Harley

Judith Harley, MA, is a physics graduate and freelance computer consultant who advises on, and designs, commercial and private database, spreadsheet, and Visual Basic applications. She has taught computing courses at Oxford University for over 20 years.

Course aims

  • To introduce many and varied applications of artificial intelligence to society
  • To discuss the challenges and pitfalls faced by artificial intelligence applications
  • To evaluate the tangible impact of artificial intelligence on humanity, now and in the future

Learning outcomes

By the end of this course, students should:

  • Understand the scope and reach of artificial intelligence applications
  • Understand the conceptual, practical and ethical challenges facing AI applications
  • Have detailed knowledge of lessons learned from specific AI applications
  • Be able to generalise examples of real-world AI applications to new domains
  • Be able to assess the potential impact of AI on significant problems critically

Assessment methods

You will be set two pieces of work for the course. The first of 500 words is due halfway through your course. This does not count towards your final outcome but preparing for it, and the feedback you are given, will help you prepare for your assessed piece of work of 1,500 words due at the end of the course. The assessed work is marked pass or fail.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link:


Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an Enrolment form for short courses | Oxford University Department for Continuing Education

Level and demands

FHEQ level 4, 10 weeks, approx 10 hours per week, therefore a total of about 100 study hours.

IT requirements

This course is delivered online; to participate you must to be familiar with using a computer for purposes such as sending email and searching the Internet. You will also need regular access to the Internet and a computer meeting our recommended minimum computer specification.