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Training AI with Humans
  • Level Intermediate
  • Course by Johns Hopkins University
  • Offered by

About

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.

Skills you learn

Modules

Untitled Lesson

    • 1 Readings
    • 1 Plugin
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1 Readings

  • Course Overview

Introduction to Machine Learning

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Introduction to Machine Learning

2 Videos

  • Machine Learning
  • Models

1 Readings

  • Reading References

Evaluating and Constructing ML Classifiers

    • 3 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Evaluating and Constructing ML Classifiers

3 Videos

  • Operationalize Data
  • Data Normalization
  • Decision Tree

1 Readings

  • Reading References

Module-end Assessments

    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Machine Learning

1 Labs

  • Practice Lab - Machine Learning Classifier to Predict in R

Understanding Inter-Annotator Agreement (IAA)

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Understanding Inter-Annotator Agreement (IAA)

2 Videos

  • Inter-Annotator Agreement (IAA) Examples
  • Inter-Annotator Agreement (IAA) Measures

1 Readings

  • Reading References

Calculating and Implementing IAA

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Calculating and Implementing IAA

1 Videos

  • Inter-Annotator Agreement (IAA) Calculation

1 Readings

  • Reading References

Module-end Assessments

    • 1 Assignment
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1 Assignment

  • Inter-Annotator Agreement (IAA)

Introduction to Crowdsourcing

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Introduction to Crowdsourcing

2 Videos

  • Crowdsourcing
  • Amazon Mechanical Turk

Setting Up and Designing Crowdsourcing Tasks

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Setting Up and Designing Crowdsourcing Tasks

2 Videos

  • Experimentation
  • Tutorial on setting up your first AMT account

Module-end Assessments

    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Crowdsourcing

1 Labs

  • Practice Lab: Impact of Payment & Complexity on Crowdsourcing Task Efficiency

1 Readings

  • Reading References

Designing Crowdsourcing Studies with AMT

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Designing Crowdsourcing Studies with AMT

1 Videos

  • Design of Experiments

1 Readings

  • Reading References

Collecting and Analyzing AMT Data

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Collecting and Analyzing AMT Data

1 Videos

  • AMT Addiction

1 Readings

  • Reading References

Module-end Assessments

    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Platforms

1 Labs

  • Practice Lab: Neuroscientific Explanation of Addiction - Analyzing Stigma, Dangerousness, & Social Distance

1 Readings

  • Self-Reflective Reading: Personal Reflection on Platforms

Impact of Inter-Annotator Agreement on ML Performance

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Impact of Inter-Annotator Agreement on ML Performance

2 Videos

  • Data Myths and the R.O.A.D. Framework
  • Case Study: COVID Test Kit Mailing

1 Readings

  • Reading References

Designing Effective Crowdsourcing for ML Improvement

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Designing Effective Crowdsourcing for ML Improvement

2 Videos

  • Case Study: Organ Transplant
  • Case Study: COVID Case Count Estimation

1 Readings

  • Reading References

Module-end Assessments

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Crowdsourcing and Machine Learning

1 Readings

  • Self-Reflective Reading: Crowdsourcing and Machine Learning
Ian McCulloh
Instructor

Ian McCulloh