Machine Learning and Economic Value

Overview

• Machine learning creates significant economic value.
• 99% of this value comes from supervised learning.

Supervised Learning

• Definition: Algorithms learn input-output mappings (X to Y).
• Key feature: The algorithm learns from examples with correct answers (labels Y).
• Process:
  • Given input (X) and the correct output (Y), the algorithm learns to predict Y from new X.

Examples of Supervised Learning Applications

1. Spam Detection
   • Input: Email (X)
   • Output: Spam or not spam (Y)
2. Speech Recognition
   • Input: Audio clip (X)
   • Output: Text transcript (Y)
3. Machine Translation
   • Input: English text (X)
   • Output: Translated text (Y) in various languages.
4. Online Advertising
   • Input: Ad data and user info (X)
   • Output: Likelihood of ad click (Y).
   • Significant revenue driving for companies.
5. Self-Driving Cars
   • Input: Images and sensor data (X)
   • Output: Positions of other cars (Y).
6. Visual Inspection in Manufacturing
   • Input: Image of product (X)
   • Output: Defect status (Y).

Training Models

• Train models with pairs of inputs (X) and outputs (Y).
• After training, the model predicts new outputs for unseen inputs.

Example Case: Housing Price Prediction

• Data collected shows:
  • Horizontal axis: Size of house (sq ft)
  • Vertical axis: Price of house (thousands of dollars)
• Prediction process:
  • Fit a line to data to estimate price.
  • Example predictions:
    • 750 sq ft house could be priced around $150,000 (line fit).
    • A more complex curve could estimate closer to $200,000.
• Importance of selecting the appropriate model for predictions.

Terminology

• Regression: Predicting a number from a continuous range (e.g., housing prices).
• Classification: Another major type of supervised learning to be explored next.

Conclusion

• Supervised learning encompasses various applications with significant economic impact.
• Understanding the types of problems (regression vs classification) is crucial for effective machine learning.