Machine Learning Notes - 2022 Fall
  • References
  • 1. Introduction
    • 1.1. What is Machine Learning?
    • 1.2. Basic setting for Machine learning problems
    • 1.3. Python quick guide
    • 1.4. Exercises
  • 2. k-NN
    • 2.1. k-Nearest Neighbors Algorithm (k-NN)
    • 2.2. k-NN Project 1: iris Classification
    • 2.3. k-NN Project 2: Dating Classification
    • 2.4. k-NN Project 3: MNIST Handwritten recognition
    • 2.5. Exercises and Projects
  • 3. Decision Tree
    • 3.1. Gini impurity
    • 3.2. CART Algorithms
    • 3.3. Decision Tree Project 1: the iris dataset
    • 3.4. Decision Tree Project 2: make_moons dataset
    • 3.5. Exercises and Projects
  • 4. Ensemble methods
    • 4.1. Voting machine
    • 4.2. Bootstrap aggregating
    • 4.3. AdaBoost
    • 4.4. Exercises and Projects
  • 5. Logistic Regression
    • 5.1. Basic idea
    • 5.2. Regularization
    • 5.3. Neural network implement of Logistic regression
    • 5.4. Multi class case
    • 5.5. Exercises and Projects
  • 6. Neural Network
    • 6.1. Neural network: Back propagation
    • 6.2. Example
    • 6.3. Exercises and Projects
  • 7. Appendix
    • 7.1. Datasets
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Index

C | R | S | U

C

  • Classification

R

  • Regression
  • Reinforcement Learning

S

  • Semisupervised Learning
  • Supervised Learning

U

  • Unsupervised Learning

By Xinli Xiao
© Copyright 2022.