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mbastola / PAC-Learning-foundations-in-AI

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This project dives into mathematical foundations of PAC learning theory while adding Python examples that make core ideas like generalization, sample complexity, VC dimension, halfspaces, perceptrons, and logical semantics executable and easier to inspect.

perceptron learning-theory generalization pac-learning vc-dimension sample-complexity ai-foundations theoretical-machine-learning probably-approximately-correct-learning pac-semantics
  • Updated Jun 24, 2026
  • TeX

krichelj / ML_BGU_2020

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Machine Learning and Analysis of Big Data course, Computer Science M.Sc., Ben Gurion University of the Negev, 2020

computer-science machine-learning big-data linear-programming neural-networks regularization gradient-descent bayesian-inference kernel-methods support-vector-machines clustering-algorithm quadratic-programming convex-optimization principle-component-analysis nearest-neighbours-classifier probability-statistics k-means-clustering mixture-models pac-learning vc-dimension
  • Updated Feb 11, 2023
  • MATLAB

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