Artificial Intelligence Concepts: Introduction to Machine Learning (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 Introduction to Machine Learning sheds light on the methods at the heart of the AI revolution, introducing fundamental concepts motivating Machine Learning and differentiating types of Machine Learners. This course studies various approaches covering supervised and unsupervised learning, including clustering, neural networks, and deep learning. It considers the application of Machine Learning to long-standing problems like natural language processing and the challenges and opportunities Machine Learning presents for the global economy. It is aimed at a general audience, including professionals whose work brings them into contact with AI and those with no more than a passing acquaintance with 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 makes extensive use of mathematical notation consistent with its level as a first-year undergraduate course (FHEQ level 4) in a mathematically-inclined discipline, such as economics, engineering, or computer science. The course does not involve any coding and instead focuses on concepts in Artificial Intelligence for a general audience.

Programme details

Unit 1: Computational learning theory

  • Mathematically formalising the process of learning
  • The theory behind machine learning algorithms
  • PAC: Probably Approximately Correct learning
  • Hypothesis spaces and model accuracy
  • The theoretical lower bound in learning theory


Unit 2: Associative memories

  • Listing versus associative memories
  • Data storage in biological neural networks
  • The Hopfield model with the Hebbian and Storkey learning rules
  • Capacity and Hopfield networks


Unit 3: Supervised learning

  • Identifying and formulating supervised learning problems
  • Passing inputs and outputs to learning algorithms
  • Performance metrics for learning algorithms
  • The theoretical limitations of supervised learning


Unit 4: Unsupervised learning

  • Supervised versus unsupervised learning
  • K-means clustering
  • Methods of hierarchical clustering
  • The Apriori algorithm
  • Principal component analysis


Unit 5: Reinforcement learning

  • Concepts and terminologies in reinforcement learning
  • Markov decision processes and Bellman equations
  • Implementing reinforcement learning
  • Understanding Q-learning
  • Identifying applications of reinforcement learning


Unit 6: Neural networks

  • The analogy between the human brain and artificial neural nets
  • The McCulloch-Pitts neuron
  • The architecture of a multilayer perceptron
  • Loss functions and the backpropagation algorithm
  • Training neural networks


Unit 7: Deep learning

  • Deep learning methods and representation learning
  • Convolution and pooling layers for image recognition
  • The inner workings of recurrent neural networks


Unit 8: Natural Language Processing

  • Defining natural language processing
  • Classifying natural language processing applications
  • Real-world applications of natural language processing


Unit 9: Where Machine Learning can go wrong

  • How biases arise in data collection and analysis
  • The misuse and malicious use of machine learning
  • Poor model fitting: underfitting and overfitting
  • Defending against attacks on machine learning algorithms


Unit 10: Effects of AI on the world economy

  • The historical impact of innovation on job markets and the global economy
  • Expert predictions on the effects of Artificial Intelligence innovation
  • Winners and losers: fields and occupations impacted by Artificial Intelligence
  • Global domestic product and the economic impact of Artificial Intelligence
  • The AI revolution, policymakers and governments


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


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 fundamental concepts underpinning Machine Learning approaches
  • To study a wide variety of Machine Learning tools, from clustering to Deep Learning
  • To identify the opportunities and challenges presented by modern Machine Learning methods

Learning outcomes

By the end of this course, students should:

  • Understand the concepts and theories underpinning Machine Learning
  • Understand the different types of Machine Learners
  • Have detailed knowledge of specific Machine Learning methodologies
  • Be able to identify suitable Machine Learning approaches for real-world applications
  • Be able to critically assess the potential impact and pitfalls of Machine Learning tools.

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.