Machine Learning

Motivation

  • AI goal: replace human programming with "self-programming" (= predict appropriate behavior based on experience)

  • The example: infants

    • language skills
    • motor skills
    • other behaviors
  • Usual dichotomy:

    • Algorithmic/heuristic "tricks"

    • Simulate human behavior (infant brain)

ML and Systems

  • Data flow in ML systems

    • Supervised: Train first, then use

    • Reinforcement: Train during use

  • Data complexity

    • Boolean, discrete, continuous

    • This course mostly Boolean

  • ML system evaluation

    • statistical significance

    • negative and positive examples

    • Type I (false-positive) and Type II (false-negative) error

    • Receiver Operating Characteristic (ROC) curves

Modes Of Learning

  • "Discovery" learning and "generalization" learning

    • TD-Gammon

    • Relation between discovery learning and generalization learning

  • Deductive learning: concluding things from principles

    • Theorem proving

    • Knowledge compilation

    • Deductive "KR-based" learning in some database

    • Basic idea: cache most general easily-derivable version of result in case similar query later

Inductive learning

  • Supervised vs. reinforcement

  • Training, validation (test), and classification data

  • Overtraining and overfitting

  • Ensemble learning: e.g. boosting

Last modified: Monday, 4 November 2019, 2:49 PM