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How to Tutorials

  • How to: Machine Learning
    • Supervised Learning
      • Linear Regression
      • Logistic Regression
      • k-Nearest Neighbours
      • Random Forest
      • Bayesian Linear Regression
      • Gaussian Process Regression
    • Unsupervised Learning
      • k-means
  • How to: Reinforcement Learning
    • Introduction
      • Problem Setup
      • Value Function
      • Action-Value Function
    • Value Iteration
      • What is Value iteration?
      • Markov Decision Processes
      • Example Environment - Gridworld
      • Computing the Value Function and Policy
      • Summary

Advanced Applications

  • Advanced: Machine Learning
  • Advanced: Reinforcement Learning

Theories

  • Regression
  • Classification

Appendix

  • A: Linear Algebra
  • B: Vector and Matrix Identities
  • C: Vector and Matrix Calculus
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