Introduction to machine learning

Machine learning increasingly influences decisions about what we see, buy, and are offered; often without us noticing.

This one-day course provides a clear, hands-on introduction to how machine learning systems are trained, how they make decisions, and why they sometimes fail.

Participants will explore real examples, experiment with simple models, and learn how to critically evaluate AI-driven systems in everyday and professional contexts.

Machine learning actively shapes modern life, from recommendations and advertising to hiring, finance, healthcare, and public policy. Yet many people interact with these systems without understanding how they work, what their limitations are, or how much human judgment is involved in their design.

This one-day introductory course demystifies machine learning for a non-technical audience. Through interactive demonstrations, hands-on experiments, group discussions, and guided reflection, participants will learn how machine learning models are trained using data, how they classify and predict information, and how design choices can introduce errors or bias.

The course emphasizes conceptual understanding, critical thinking, and real-world impact. Participants will examine how machine learning systems can fail, why ethical considerations matter, and when human oversight is essential. By the end of the day, learners will be better equipped to ask informed questions, evaluate AI claims, and understand the role machine learning plays in decision-making processes.

• I understand how machine learning systems are trained using data, labels, and human-defined rules.
• I can explain how machine learning models make predictions and why their outputs are probabilistic rather than certain.
• I can identify common causes of error, bias, and failure in machine learning systems and recognize their real-world consequences.
• I can critically evaluate the use of machine learning in everyday and professional contexts and ask informed questions about its ethical and practical implications.

Module 1 – What is Machine Learning

• AI vs machine learning vs automation
• Where machine learning works well — and where it does not
• Pattern recognition, prediction, and uncertainty

Module 2 – How Machines Learn from Data

• Training data, labels, and supervision
• Classification and decision boundaries
• Hands-on experiment with a simple machine learning model

Module 3 – Testing, Errors, and Model Failure

• Why models make mistakes
• Overfitting, ambiguity, and edge cases
• Stress-testing models with unexpected inputs

Module 4 – Human Judgment and Bias in AI Systems

• How human choices shape algorithms
• Bias in data and labeling
• When models should refuse to decide

Module 5 – Ethics, Responsibility, and Real-World Impact

• Machine learning in hiring, finance, surveillance, and media
• Accountability and transparency
• Group debate on ethical and societal implications