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Subsymbolic AI (machine learning)

Topics

Machine learning foundations

  • How machine learning works at a general level
  • Learning paradigms
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Neural networks
  • Neuro-symbolic AI

Weekly Discussion: Identifying Machine Learning in Everyday Contexts

Please submit all graded work via Canvas. Participation requirements and grading details are provided in Canvas.

Step 1: Choose a familiar context

Select one real-world example you have encountered from your work or daily life. It may already use machine learning, or it may be a system you believe could benefit from it.

Examples include:

  • Email spam filtering
  • Fraud detection in banking
  • Predictive text or autocomplete
  • Social media feed ranking
  • ……

Briefly describe:

  • What the system or situation is
  • What problem it is trying to solve
  • What kinds of decisions or predictions are involved

Step 2: Why machine learning fits this problem

Explain why this scenario is suitable for a machine learning approach.

You may consider:

  • Are there patterns that could be learned from past data?
  • Is the task about prediction, classification, ranking, or grouping?
  • Would writing explicit rules be difficult or impractical?
  • Does the system need to adapt over time?

Clarify whether the task would likely involve:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

No technical detail is needed, but your reasoning should be clear.

Step 3: How the system would be adapted for machine learning

Think concretely about how this problem would be operationalized.

You may address:

  • What would count as the training data?
  • What are the inputs (features)?
  • What is the output or prediction?
  • How would performance be evaluated?
  • What risks of overfitting or bias might arise?

Focus on the logic of learning from data, not on programming details.

Step 4: Limits and considerations

Reflect on potential limitations or risks.

  • Could the system make confident but incorrect predictions?
  • What kinds of bias might appear in training data?
  • Would transparency or interpretability matter?
  • Would a rule-based approach be preferable in some cases?

Consider both benefits and trade-offs.

Expected outcome

Your post should present a clear analysis of one scenario, showing:

  • why machine learning is appropriate (or not),
  • how it would learn from data,
  • and what practical or ethical considerations might arise.

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