This course explores the intersection of machine learning (ML) and human input through various methodologies and tools. Students will gain a comprehensive understanding of machine learning techniques.

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.

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.

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.

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.

This module explores the intersection of crowdsourcing and ML performance enhancement. Evaluate how Inter-Annotator Agreement (IAA) affects ML model reliability and accuracy.