Johns Hopkins University
Training AI with Humans
Johns Hopkins University

Training AI with Humans

This course is part of Social Computing Specialization

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

22 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

22 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn to construct and evaluate various machine learning classifiers and performance metrics.

  • Master the calculation and implications of Inter-Annotator Agreement (IAA) for data consistency.

  • Understand how to design and implement effective crowdsourcing tasks using Amazon Mechanical Turk.

  • Analyze crowdsourced data to enhance machine learning models and understand ethical considerations in AI.

Details to know

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Recently updated!

September 2024

Assessments

15 assignments

Taught in English

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This course is part of the Social Computing Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
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There are 6 modules in this course

This course explores the intersection of machine learning (ML) and human input through various methodologies and tools. Spanning five modules, students will gain a comprehensive understanding of machine learning techniques, the role of human annotation in ML performance, and the principles and practices of crowdsourcing. The course covers key aspects of designing and implementing crowdsourced studies, calculating inter-annotator agreements, and leveraging crowdsourcing to enhance ML performance. Practical skills will be developed through hands-on activities using platforms like Amazon Mechanical Turk (AMT) and analyzing the data collected from such platforms.

What's included

1 reading1 plugin

In this module, you’ll be introduced to the fundamentals of machine learning (ML). You’ll learn the definition and principles of ML, and gain practical skills in calculating and comparing ML performance metrics. You’ll get a chance to understand how to construct ML classifiers and analyze their effectiveness across different algorithms. This module prepares you to apply ML techniques effectively in various domains, enhancing your ability to solve complex problems using data-driven approaches.

What's included

5 videos2 readings3 assignments1 ungraded lab

In this module, you’ll explore the significance of IAA in Machine Learning (ML) performance. You’ll learn to calculate IAA manually and implement Krippendorf’s Alpha using the software. You’ll gain insights into how IAA impacts the reliability of annotated data and its implications for ML model training. This module equips you with essential skills to ensure consistency and reliability in data annotation processes, crucial for effective ML applications.

What's included

3 videos2 readings3 assignments

In this module, you’ll be introduced to the concept and practical applications of crowdsourcing. You’ll get a chance to learn how crowdsourcing enhances problem-solving through collective efforts and explore real-world use cases. You’ll be able to establish your first Amazon Mechanical Turk (AMT) account and understand the platform's capabilities for executing crowdsourced tasks. You’ll get a chance to delve into crowdsourcing design principles to optimize task efficiency and reliability. This module prepares you to leverage crowdsourcing effectively for diverse applications, from data annotation to research experiments.

What's included

4 videos1 reading3 assignments1 ungraded lab

Platform" module focuses on leveraging Amazon Mechanical Turk (AMT) for crowdsourcing studies. Learn to design effective experiments using AMT, ensuring optimal task design and participant engagement. Collect data through AMT and perform initial analyses to derive meaningful insights from crowdsourced data. Understand the implications of AMT addiction and ethical considerations in platform-based research. This module equips you with practical skills to conduct reliable and insightful crowdsourcing studies using AMT.

What's included

2 videos3 readings3 assignments1 ungraded lab

This module explores the intersection of crowdsourcing and ML performance enhancement. Evaluate how Inter-Annotator Agreement (IAA) affects ML model reliability and accuracy. Explore case studies such as COVID test kit distribution and organ transplant matching to understand real-world applications. Learn to optimize ML performance through effective crowdsourcing design, ensuring data quality and reliability in machine learning applications.

What's included

4 videos3 readings3 assignments

Instructor

Ian McCulloh
Johns Hopkins University
6 Courses12 learners

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