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  • Machine learning is a subset of AI. 
    • Simplistic description:  a pattern recognition system.
  • Probability and Statistics:  used for:
    • Inference
    • Prediction
    • Clustering ?
  • optimization
    • example
      • y = mx + b
        • in machine learning you are trying to find the best answer for m
        • b is used to account for obscurities and bias in the model
        • x is the input
        • y is the result.
  • two main ways of learning
    • Discriminative model
      • decision boundary
      • uses:
        • Regressions
        • Support Vector Machines
      • most deep neural nets are discriminative models
    • Generative model
  • What ML can't do
    • learn without sufficient data
    • explain itself
    • reason
      • cannot understand that if a < b and b < c, that a < c
    • "Do the right thing" that is change break from the task to do something that is important but out of "character" e.g. a protestor stopping to help an injured police officer.
  • What ML can do
  • Question about the machine not being able to explain what it is doing.
  • Bias vs Variance tradeoff
    • to minimize error rates you want to minimize the bias and variance.
      • less bias has more variance
      • less variance has more bias