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Week 2: AI Systems & Data

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#AI systems  #machine learning  #data 

Week 2: AI Systems & Data#

Focus: How AI systems work; data and training

📚 Required Readings#

Primary Readings#

  1. “How Machine Learning Works: The Training Process” (25 min)

    • Training data, features, and labels
    • Algorithms and parameter optimization
    • Testing and validation

  2. “Data: The Fuel of AI” (20 min)

    • Data collection and preparation
    • Data quality and representation
    • The role of big data in modern AI

Supplementary Resources#

  • “The Hidden Technical Debt in Machine Learning Systems” — Selected sections
  • Interactive visualizations: Neural network playground

💭 Discussion Prompts#

  1. Why is data quality more important than data quantity?
  2. How do training decisions affect AI system behavior in the real world?
  3. What role does feedback play in AI systems over time?

📝 Preparation for Class#

  • Find a dataset related to a topic you care about (e.g., Kaggle, UCI Machine Learning Repository)
  • Reflect on what patterns you’d hope an AI system would find in that data
  • Consider: What could go wrong if the data was biased or incomplete?

🔗 Related Assignment#

See Week 2 Assignment for this week’s task.