Artificial Intelligence Concepts: An Introduction (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 organizations, from governments to big business. However, these technologies pose challenges including social and ethical dilemmas.

This course provides an essential introduction to the key topics underpinning AI, including its historical development, theoretical foundations, basic architecture, modern applications, and ethical implications. The course investigates the future trajectory of AI and considers its potential for improving the world while highlighting pitfalls and limitations. 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. The course aims to confer an appreciation of the ways in which our world has already been transformed by AI, to explain the fundamental concepts and workings of AI, and to equip us with a better understanding of how AI will shape our society, so 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: What is intelligence?

  • The concept of intelligence
  • What is artificial intelligence?
  • Weak vs Strong AI
  •  A Brief History of AI
  • Artificial Intelligence Revived
  • The Golden Age of AI
  •  Applications of AI

Unit 2: Artificial intelligence and society

  • Data governance
  • AI and Equality
  • AI and employment
  • Economic opportunities of AI
  • Risks of AI
  • AI and accountability

Unit 3: Systems and agents

  • Concept of an agent
  • Structure of an agent
  • Rationality of an agent
  • Perfect agents
  • Task environments
  • Designing agents
  • Simple reflex agent
  • Model-based reflex agents
  • Goal-based agents
  • Utility-based agents

Unit 4: Logic and language

  • Early ideas: Logic and language
  • Imitating mathematical intelligence
  • Propositional logic
  • Designing mathematical languages
  • Gödel's incompleteness
  • Solving mathematical problems with AI
  • The Halting problem

Unit 5: Expert systems

  • What are expert systems?
  • Representing knowledge
  • Reasoning with logic
  • Backward chaining
  • Advantages and disadvantages of expert systems

Unit 6: Connectionist models

  • Biological neural network
  • ANNs in action
  • Building blocks of a neural network
  • An example of a neural network
  • Backpropagation
  • Architectures and training

Unit 7: Artificial intelligence in the 21st century

  • Arriving at the current state of AI
  • Big Data
  • Big Data and the Internet
  • AI and healthcare
  • AI and automobiles
  • Cybersecurity
  • Machine translation
  • Ethics of AI

Unit 8: Data science and artificial intelligence

  • What is data science?
  • Data science processes
  • Data exploration: An example
  • AI methods in data science
  • Autoencoders
  • Data imputation

Unit 9: Machine learning and artificial intelligence

  • What is machine learning?
  • Supervised learning
  • Unsupervised learning
  • Reinforcement Learning

Unit 10: Testing artificial intelligence systems

  • Why test AI systems?
  • Software lifecycle costs
  • Increasing adoption of AI/ML
  • Uncertainty and Oracles
  • Ethical dilemmas
  • Adversarial inputs
  • Challenges of testing AI
  • Testing in machine learning
  • AI within wider systems

We strongly recommend that you try to find a little time each week to engage in the online conversations (at times that are convenient to you) as the forums are an integral, and very rewarding, part of the course and the online learning experience.


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


Dr Noureddin Sadawi

Dr Noureddin Sadawi specialises in machine/deep learning and data science. He has several years’ experience in various areas involving data manipulation and analysis. He received his PhD from the University of Birmingham. He is the winner of two international scientific software development contests - at TREC2011 and CLEF2012.

Noureddin is an avid scientific software researcher and developer with a passion for learning and teaching new technologies. He is an experienced scientific software developer and data analyst. Over the last few years, he has been using R and Python as his preferred programming languages.

He has also been involved in several projects spanning a variety of fields such as bioinformatics, textual/image/video data analysis, drug discovery, omics data analysis and computer network security. He has taught at multiple universities in the UK and has worked as a software engineer in different roles. Currently he holds the following part-time roles: senior content developer and lecturer at the University of London; international trainer with O'Reilly and Pearson; short course trainer and instructor at Goldsmiths University, London as well as a lecturer at the University of Oxford. He is the founder of SoftLight LTD, a London-based company that specialises in data science and machine/deep learning where he works as a consultant providing advice and expertise in these areas. Currently he is a member of the organising committee of this international conference: A list of his publications can be found here.

Course aims

  • To introduce the concept of artificial intelligence and its different paradigms
  • To provide an understanding of the real-world potential and limitations of artificial intelligence
  • To describe the reach of artificial intelligence in society today.

Learning outcomes

By the end of this course students should:

  • Understand the concept of artificial intelligence versus human intelligence
  • Understand the foundational concepts in mathematics and logic underpinning AI
  • Be able to identify examples of real-world applications of AI
  • Be able to discuss the social, ethical and sustainability dilemmas posed by AI
  • Understand some of the challenges, limitations, and pitfalls of AI in real world applications.

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 application form.

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.