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AI and Machine Learning

Artificial Intelligence often feels like magic — but at its core, it’s built on clear mathematical and computational principles. This module takes you inside the “black box” of AI and Machine Learning to understand how machines learn, make predictions, and adapt to data.
You will explore the three fundamental types of AI and Machine Learning — supervised learning, unsupervised learning, and reinforcement learning — learning not just how they work, but when and why to use each.
By the end, you’ll have demystified AI and Machine Learning’s decision-making process and gained the knowledge to evaluate machine learning models in both technical and business contexts.

Oxford University AI and Machine Learning

You’ll Learn in AI and Machine Learning

  1. The Core Principles of AI and Machine Learning
    • How algorithms detect patterns, make predictions, and improve over time.
    • The importance of training data, model selection, and evaluation metrics.
  2. Supervised Learning
    • Learn how labelled data teaches machines to classify and predict outcomes.
    • Applications: spam email detection, medical diagnosis, and price forecasting.
  3. Unsupervised Learning
    • Discover how AI and Machine Learning find hidden patterns without labelled examples.
    • Applications: customer segmentation, anomaly detection, and market basket analysis.
  4. Reinforcement Learning
    • Understand how AI agents learn through trial and error, receiving rewards or penalties.
    • Applications: autonomous driving, robotics, and game-playing AI (like AlphaGo).
  5. Interpreting the “Black Box”
    • Explore explainable AI (XAI) and why transparency matters in decision-making.
    • Learn how to balance accuracy, fairness, and interpretability.

Key Topics Covered

  • How data fuels AI and Machine Learning models
  • The training, validation, and testing process
  • Strengths and weaknesses of each learning type
  • Real-world examples from business, healthcare, finance, and technology
  • The role of ethics and bias in AI and Machine Learning

Why This Module Matters

Many leaders know AI and Machine Learning is powerful but can’t explain how it works — making it hard to trust or effectively deploy. By breaking down the mechanics of machine learning, this module helps you bridge the gap between technical teams and business strategy, ensuring AI solutions are transparent, ethical, and aligned with organisational goals.

Practical Outcome

By completing Module 2, you will:

  • Confidently explain how supervised, unsupervised, and reinforcement learning work.
  • Identify which type of learning best fits different business problems.
  • Understand how to ask the right questions when working with AI and Machine Learning developers or vendors.
  • Gain the ability to spot potential risks like data bias or overfitting early.
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