Draft: What Humans Can Learn From Machine Learning

Human Learning differs from Machine Learning in important ways.

However, there are useful things we can take from ML

  • the alpha parameter
  • momentum: https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Momentum
  • optimality in the face of an impossibly large search space
  • generalize > memorize by using a testing set
  • epsilon exhaustion and Probably Approximately Correct learning
  • naive bayes - how can it be right
    • So you just need to be directionally correct.
  • random restart hill climbing
    • if theres a v specific point that is superbly better than others, you're in a bad world
  • simulated annealing - hot and cold
  • eager learning vs lazy learning

https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRpiXLm24rgkZJbENxamD6f3ZDJfK7viU5gbhoGrwj1jp-AMBXVVg https://swizec.com/blog/only-self-help-business-advice-you-need/swizec/7190


⚠️ You are reading an unpublished, incomplete draft. Questions are welcome but feedback/criticism may be premature.