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 <- needed this source
- 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
convert definites to maximum likelihood/probability densitiy estimation?
- transfer learning as a bootstrap -> genetic evolution?
- eager learning vs lazy learning
- policy CAN change depending on whether youre playing with infinite horizon
- temporal attribution problem
- MDP => Bellman Equation => Value iteration strategy (rewards vs utiltiy vs always adding truth)
- PLANNER: model -> policy
- LEARNER: transitions -> policy
- MODELER: transition -> model
- SIMULATOR: model -> transition
- exploration/exploitation/explain dilemma
- conv nets - move stuff around - translational invariance
- rnn - languages
- starcraft etc alphazero were total untrained
- given infinite time and data ofc we let total untrain
- but dont have infinite data so helps to bootstrap
human in the loop
⚠️ You are reading an unpublished, incomplete draft. Questions are welcome but feedback/criticism may be premature.