Training AI with Humans
Master machine learning and crowdsourcing techniques to enhance AI performance through human collaboration. Learn IAA analysis and AMT implementation.
Master machine learning and crowdsourcing techniques to enhance AI performance through human collaboration. Learn IAA analysis and AMT implementation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Social Computing Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
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What you'll learn
Construct and evaluate machine learning classifiers and performance metrics
Master Inter-Annotator Agreement (IAA) calculation and analysis
Design and implement effective crowdsourcing tasks using Amazon Mechanical Turk
Analyze crowdsourced data to enhance machine learning models
Understand ethical considerations in AI and crowdsourcing
Skills you'll gain
This course includes:
5.35 Hours PreRecorded video
15 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores the intersection of machine learning and human collaboration, focusing on enhancing AI performance through effective data annotation and crowdsourcing. Students learn machine learning fundamentals, Inter-Annotator Agreement techniques, and practical implementation using Amazon Mechanical Turk. The curriculum covers experimental design, ethical considerations, and real-world applications through case studies in healthcare and research, providing hands-on experience in optimizing AI models through human input.
Course Introduction
Module 1 · 14 Minutes to complete
Machine Learning
Module 2 · 5 Hours to complete
Inter-Annotator Agreement (IAA)
Module 3 · 4 Hours to complete
Crowdsourcing
Module 4 · 3 Hours to complete
Platforms
Module 5 · 4 Hours to complete
Crowdsourcing and Machine Learning
Module 6 · 4 Hours to complete
Fee Structure
Instructor
Pioneering Social Network Analysis and AI at Johns Hopkins University
Dr. Ian McCulloh is an esteemed associate professor at Johns Hopkins University, holding joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. His research focuses on social neuroscience, social network analysis, and the application of artificial intelligence to enhance understanding of online influence and strategic communication. With over 100 peer-reviewed publications and several influential books, including Social Network Analysis with Applications and ISIS in Iraq: Understanding the Social and Psychological Foundations of Terror, Dr. McCulloh has established himself as a leading voice in his field. He also founded the Brain Rise Foundation, a nonprofit dedicated to advancing neuroscience research for substance abuse recovery. Prior to his academic career, he had a distinguished military service, retiring as a Lieutenant Colonel after 20 years, during which he led innovative projects in data-driven social science research for countering extremism. Dr. McCulloh's multifaceted expertise and commitment to applying science for societal benefit make him a valuable asset to both academia and public health initiatives.
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Frequently asked questions
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