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• Machine learning is a subset of AI. 
  • Simplistic description:  a pattern recognition system.
• Probability and Statistics:  used for use data sample to do:
  
  • Inference
  • Prediction
  • Clustering ?
  • Classification
• 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
    • probability distributions of the data set
    • uses:
      • Naive Bayes (https://en.wikipedia.org/wiki/Bayes%27_theorem)
      • Generative Adversarial Network
• 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
  • Repetitive tasks really fast
  • Drive you crazy (takes a lot of effort to make your model work correctly)
  • Generate synthetic data
    • including pictures, video, art, poems, etc.
    • Example: https://www.thispersondoesnotexist.com/
      • Wikipedia article about the above site and the GAN network it uses:  https://en.wikipedia.org/wiki/StyleGAN
• Question about the machine not being able to explain what it is doing.
  • Discussion about this question brought up this link:
  • https://cacm.acm.org/magazines/2020/1/241703-techniques-for-interpretable-machine-learning/fulltext
• 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