Section outline

  • Lesson Goal: Introduce what machine learning is and how it differs from traditional programming, including key concepts and real-world examples (medical diagnosis and a green screen effect) that illustrate how we tell a computer what we want through data and objectives.

    • Micro-Topic 1.1: Programming vs. Learning

      Goal: Understand the difference between explicitly programming a computer and having it learn from data, and why the latter is powerful in an AI-driven world.

    • L01 MT1 FREE content only SCORM12 SCORM package

    • Micro-Topic 1.2: Key Concepts of Machine Learning

      Goal: Introduce fundamental ML concepts and terminology (algorithm, model, training, dataset, and types of learning) to build a foundation for deeper topics.

    • L01 MT2 FREE content only SCORM12 SCORM package

    • Micro-Topic 1.3: A Brief History of Machine Learning

      Goal: Highlight key milestones in the development of machine learning, to show how the field evolved and why it’s so influential today.

    • L01 MT3 FREE content only SCORM12 SCORM package

    • Micro-Topic 1.4: Example – ML for Medical Diagnosis

      Goal: See how machine learning can assist in predicting or diagnosing medical conditions (like diabetes) by finding patterns in patient data that might be hard for humans to program explicitly.

    • L01 MT4 FREE content only SCORM12 SCORM package

    • Micro-Topic 1.5: Example – Green Screen Effect (Traditional vs ML)

      Goal: Compare a traditional programming solution to a visual effect (green screen background removal) with a machine learning solution, illustrating how ML can simplify complex tasks by learning from data.

    • L01 MT5 FREE content only SCORM12 SCORM package