Artificial Intelligence: Applications (Online)

Overview

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.

Programme details

Unit 1: Project management for AI

  • Software project management
  • Considerations in AI project management
  • Different approaches to AI project management
  • 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

Certification

To earn credit (CATS points) for your course you will need to register and pay an additional £10 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 £10 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.

Assignments are not graded but are marked either pass or fail.

All students who successfully complete this course, whether registered for credit or not, are eligible for a Certificate of Completion. Completion consists of submitting the final course assignment. Certificates will be available, online, for those who qualify after the course finishes.

Fees

Description Costs
Course Fee £324.00
Take this course for CATS points £10.00

Tutor

Dr Noureddin Sadawi

Dr. Noureddin Sadawi is a consultant 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 OReilly and Pearson; short course trainer and instructor at Goldsmith University, London. He is the founder of SoftLight LTD, a London-based company that specialises in data science and machine/deep learning.

A list of his publications can be found here.

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

Application

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.