AI for Good: Solutions for Sustainability, Health, and Aid

via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

21 hours 46 minutes
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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Based on the book, AI for Good, by Juan M. Lavista Ferres and William B. Weeks. Artificial intelligence is transforming how we address the world's most pressing challenges, from sustainability to healthcare. This course explores AI’s significant role in tackling climate change, improving disaster relief, and enhancing healthcare. By using real-world examples, it provides actionable insights into how AI is improving global well-being and creating positive social impact. The course dives into practical strategies for implementing AI responsibly, emphasizing fairness, transparency, and collaboration to ensure AI benefits all sectors. Learn how AI can be used to address critical issues such as climate change and health disparities while maintaining ethical integrity. What sets this course apart is its blend of theory and practical applications, showing how AI is making a tangible difference in various domains. By examining the intersection of AI with social good, the course provides a roadmap for future advancements and solutions for a sustainable world. This course is designed for business leaders, policymakers, humanitarian organizations, and anyone interested in using AI to address global challenges. A basic understanding of AI is helpful, though the course is accessible to both technical and non-technical learners. Copyright © 2024 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Syllabus

What Is Artificial Intelligence and How Can It Be Used for Good

In this section, we define intelligence as learning ability, analyze data filtering mechanisms, and evaluate AI's potential to enhance societal good through unbiased analysis and decision-making.

Artificial Intelligence: Its Application and Limitations

In this section, we explore machine learning as data-driven rule generation, emphasizing its practical applications and limitations, including bias, generalization, and ethical considerations.

Commonly Used Processes and Terms

In this section, we explore common data analysis processes, define key performance measures, and outline the course structure for effective learning.

Deep Learning with Geospatial Data

In this section, we explore deep learning applications for geospatial data, focusing on pattern recognition, model implementation, and data processing workflows for environmental and remote sensing tasks.

Nature-Dependent Tourism

In this section, we examine nature-dependent tourism's environmental impacts, evaluate sustainable management methods, and analyze findings for conservation strategies.

Wildlife Bioacoustics Detection

In this section, we explore bioacoustic monitoring techniques for wildlife detection. We analyze sound data and design systems for species identification, enhancing conservation and ecological research.

Using Satellites to Monitor Whales from Space

In this section, we explore satellite data analysis for whale tracking, remote sensing techniques for marine monitoring, and conservation strategies using satellite information.

Social Networks of Giraffes

In this section, we examine giraffe social network structures using behavioral data collection and network analysis. The focus is on understanding wildlife interactions and their implications for conservation.

Data-Driven Approaches to Wildlife Conflict Mitigation in the Maasai Mara

In this section, we explore data collection and analysis for wildlife monitoring, identifying spatial conflict patterns and designing predictive mitigation strategies to enhance conservation and community safety.

Mapping Industrial Poultry Operations at Scale

In this section, we explore remote sensing techniques for mapping industrial poultry operations. Key concepts include scalable methods, spatial data analysis, and applications in agricultural planning.

Identifying Solar Energy Locations in India

In this section, we analyze solar potential using GIS data and irradiance metrics to evaluate land suitability for solar projects in India.

Mapping Glacial Lakes

In this section, we explore remote sensing techniques for mapping glacial lakes and assessing risks of glacial lake outburst floods (GLOFs) using satellite imagery and environmental monitoring.

Forecasting and Explaining Degradation of Solar Panels with AI

In this section, we explore AI models for forecasting solar panel degradation, analyzing performance factors, and designing predictive maintenance strategies to enhance renewable energy system reliability.

Post-Disaster Building Damage Assessment

In this section, we examine post-disaster building damage assessment methods, focusing on structural analysis, data collection, and safety protocols for effective recovery.

Dwelling Type Classification

In this section, we explore methods for classifying dwelling types using supervised learning and data analysis techniques. The focus is on practical applications for urban planning and real estate decision-making.

Damage Assessment Following the 2023 Earthquake in Turkey

In this section, we examine earthquake damage assessment methods, focusing on structural failure patterns and seismic impact evaluation for disaster response applications.

Food Security Analysis

In this section, we explore methods to evaluate food security, analyze regional access patterns, and design data collection frameworks for informed decision-making.

BankNote-Net: Open Dataset for Assistive Universal Currency Recognition

In this section, we explore BankNote-Net for assistive currency recognition, focusing on open datasets and machine learning models for accessibility applications.

Broadband Connectivity

In this section, we examine broadband infrastructure performance, factors affecting connectivity, and strategies for improving network access. Key concepts include reliability, deployment planning, and practical solutions for enhanced connectivity.

Monitoring the Syrian War with Natural Language Processing

In this section, we explore NLP techniques for conflict monitoring, focusing on sentiment analysis, event detection, and tracking geopolitical changes through text data.

The Proliferation of Misinformation Online

In this section, we examine patterns of misinformation spread, evaluate detection methods, and assess impacts on public trust. It provides practical strategies for identifying and mitigating false narratives.

Unlocking the Potential of AI with Open Data

In this section, we explore how open data enhances AI development by improving model reliability and ethical deployment through transparency and accessibility.

Detecting Middle Ear Disease

In this section, we explore diagnostic techniques for middle ear disease using acoustic immittance. Key concepts include analyzing data for abnormalities and designing screening protocols for early detection.

Detecting Leprosy in Vulnerable Populations

In this section, we examine strategies for early leprosy detection in vulnerable populations. Key concepts include risk factor analysis, targeted screening, and public health intervention planning.

Automated Segmentation of Prostate Cancer Metastases

In this section, we explore automated segmentation techniques for prostate cancer metastases using machine learning. The focus is on improving diagnostic accuracy and real-world application in medical imaging.

Screening Premature Infants for Retinopathy of Prematurity in Low-Resource Settings

In this section, we explore strategies for screening premature infants for retinopathy of prematurity (ROP) in low-resource settings. Key concepts include automated image analysis, mobile application development, and accessible diagnostic tools for early intervention.

Long-Term Effects of COVID-19

In this section, we examine the long-term effects of SARS-CoV-2, analyze post-COVID-19 health patterns, and evaluate clinical research methodologies to improve patient care and public health strategies.

Using Artificial Intelligence to Inform Pancreatic Cyst Management

In this section, we explore the use of AI for pancreatic cyst classification, analyzing imaging data with machine learning, and evaluating clinical performance to improve diagnostic accuracy and patient outcomes.

NLP-Supported Chatbot for Cigarette Smoking Cessation

In this section, we explore the use of NLP in developing chatbots for smoking cessation. Key concepts include conversational design, RCT evaluation, and real-world health applications.

Mapping Population Movement Using Satellite Imagery

In this section, we explore using satellite data to map population movement, analyze building density with remote sensing, and estimate people per structure for urban and public health applications.