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Learning Machine Learning: A Hands-On Comparison of SML, UML, AND RML
內容大綱
This exercise provides a structured framework for introducing business students to supervised machine learning (SML), unsupervised machine learning (UML), and reinforcement machine learning (RML). The exercise revolves around applying these techniques to evaluate harassment policies, offering a hands-on and practical approach to understanding how machine learning (ML) works in real-world scenarios. The exercise is suitable for undergraduate classes and graduate business classes, particularly in core information technology (IT) management, innovation management, fundamentals of AI, and governance courses.
學習目標
By working through the exercise and assignment questions, students will be able to do the following: <ul><li>Differentiate between supervised, unsupervised, and reinforcement machine learning techniques, including their strengths, limitations, and appropriate use cases</li><li>Apply ML techniques to a practical task (e.g., policy evaluation), enhancing understanding of how these methods function in real-world scenarios</li><li>Analyze the role of human judgment, data quality, and feedback in the success of ML models, particularly in subjective and evolving contexts</li><li>Appreciate the complexity and resource- and labor-intensiveness of developing good-quality artificial intelligence</li><li>Gain critical insights into selecting the appropriate ML technique based on task requirements, data availability, and business objectives</li></ul>