Oxford University

Artificial Intelligence Concepts: Practical Applications

Beginner

This course explores the practical applications of Artificial Intelligence (AI) in addressing significant global challenges. It covers critical concepts such as AI ethics and fairness, with real-world examples from disaster planning, sustainable development, and human health. Enroll to gain a comprehensive understanding of AI's transformative impact on society.

AI ethics
Machine learning
Sustainable development
Disaster management
Healthcare applications

Overview

In this course, you will delve into the diverse applications of AI, focusing on real-world case studies that highlight both the potential and challenges of AI technologies. You will learn about AI ethics, machine learning, and the role of AI in sustainable development, disaster management, and healthcare. This course is designed to equip you with the knowledge to critically assess AI's impact and stay updated with the latest developments in the field.

English
course language
Professional Certification
upon course completion

Who is this course for?

General Audience

Individuals interested in understanding the impact of AI on society and its applications.

Professionals

Professionals whose work involves interaction with AI technologies.

Beginners

Those with no prior knowledge of AI looking to gain foundational understanding.

This course offers a comprehensive exploration of AI's practical applications, focusing on real-world challenges and ethical considerations. Ideal for beginners and professionals, it provides insights into AI's transformative impact on society, equipping learners with the knowledge to engage with AI technologies effectively.

Pre-Requisites

1 / 3

Familiarity with using a computer for purposes such as sending email and searching the Internet
Regular access to the Internet and a computer meeting recommended minimum specifications
Confidence in English language proficiency

What will you learn?

Introduction to AI concepts: practical applications

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

New cohorts coming soon