Artificial Intelligence Concepts: Introduction to Machine Learning



Overview

artificial intelligence, n.

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

— Oxford English Dictionary 

Artificial Intelligence (AI) is already woven into the fabric of our lives, often in ways we barely notice. From government systems to global business, the ability of machines to sense, process, decide and learn is transforming how we live and work. But as AI evolves, so too do the ethical, social and strategic questions it raises. 

This Introduction to Machine Learning course explores the methods that power today’s AI systems. You’ll gain a clear grounding in key concepts that underpin Machine Learning, including supervised and unsupervised learning, neural networks, clustering and deep learning. We also explore how Machine Learning is being applied to areas such as natural language processing – and what this means for the future of the global economy. 

Designed for a broad audience, this course is ideal for professionals whose work intersects with AI, or for anyone looking to gain a deeper understanding of this fast-moving field – no prior experience required. 

The course does not involve any coding and instead focuses on concepts in Artificial Intelligence for a general audience and uses some maths similar to what you’d find in the first year of a university course in subjects like economics, engineering or computer science. 

This is part of a wider series exploring how AI is shaping our world, helping you speak the language of the future with confidence. 

This course has no live sessions. You will study structured materials at your own pace each week. Find out more about how our short online courses are taught.

Programme details

The course is broken down into 10 units over 10 weeks, each requiring approximately 10 hours of study time. The following topics are covered:

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

Textbooks

There is no essential reading associated with this course.

Certification

Credit Application Transfer Scheme (CATS) points 

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework. All those enrolled on an online courses are registered for credit and will be awarded CATS points for completing work at the required standard.

See more information on CATS points

Digital credentials

All students who pass their final assignment 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. 

Fees

Description Costs
Course Fee £415.00

Funding

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

Tutor

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: https://ilcict.ly/. 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.

 

Application

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.

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: https://www.conted.ox.ac.uk/about/english-language-requirements

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.

Terms & conditions for applicants and students

Information on financial support