About this Course
In the course "Training AI with Humans", you'll delve into the intersection of machine learning and human collaboration, exploring how to enhance AI performance through effective data annotation and crowdsourcing. You’ll gain a comprehensive understanding of machine learning principles and performance metrics while developing practical skills in using platforms like Amazon Mechanical Turk (AMT) for crowdsourced tasks. This unique approach combines theoretical knowledge with hands-on experience, allowing you to implement Inter-Annotator Agreement (IAA) techniques to ensure high-quality annotated data. By completing this course, you will be well-equipped to design and conduct impactful crowdsourcing studies, improving AI models in real-world applications such as healthcare and research. Whether you're looking to enhance your skills in machine learning, optimize data collection processes, or understand the ethical implications of crowdsourcing, this course offers invaluable insights and tools
What You'll Learn
- Construct and evaluate machine learning classifiers and metrics
- Master Inter-Annotator Agreement calculations
- Design and implement crowdsourcing tasks with AMT
- Analyze crowdsourced data to improve models ethically
Prerequisites
- Basic familiarity with the topic and its common terminology
- Readiness to practice through applied exercises or case-based work
Instructors
Ian McCulloh