king's college london · 7ccsmaml

Machine Learning — study guides

by Kamyar Nadarkhani

A bookish set of notes covering twenty weeks of material, from learning theory through reinforcement learning and formal verification.

Contents

  1. Week 01Learning from Data→
  2. Week 02Statistical Inference→
  3. Week 03Linear & Additive Models→
  4. Week 04Trees & Ensembles→
  5. Week 05Neural Networks — The Brain & Perceptrons→
  6. Week 07Backward Propagation & Universal Approximation→
  7. Week 08Stochastic Multi-Armed Bandits→
  8. Week 09Clustering — k-Means, DBSCAN & Dendrograms→
  9. Week 10Dimensionality Reduction→
  10. Week 11Explainable AI→
  11. Week 13Discriminative Neural Networks→
  12. Week 14Attention & Transformers→
  13. Week 15Graph Neural Networks & Unsupervised Learning→
  14. Week 16Generative Adversarial Networks→
  15. Week 17Variational Autoencoders→
  16. Week 19Reinforcement Learning I→
  17. Week 20Reinforcement Learning II→
  18. Week 21Reinforcement Learning III→
  19. Week 22Formally Verifying Neural Networks I→
  20. Week 23Formally Verifying Neural Networks II→