Machine Learning
2021

Contents:

  • 1. Mathematical Tools
  • 2. Probability and Statistics
  • 3. Overview of Machine Learning
  • 4. Dimensionality Reduction
  • 5. Regression
  • 6. Classification
    • 6.1. Bayesian Classification
    • 6.2. Nearest Neighbor Classification
    • 6.3. Logistic Regression
    • 6.4. Neural Networks
  • 7. Clustering
  • 8. Methodology of Machine Learning
Machine Learning
  • 6. Classification
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6. Classification

  • 6.1. Bayesian Classification
    • 6.1.1. Maximum a Posteriori Classifier
    • 6.1.2. Naive Bayes Classifier
      • 6.1.2.1. Discrete Naive Bayes Classifier
      • 6.1.2.2. Continuous Naive Bayes Classifier
  • 6.2. Nearest Neighbor Classification
    • 6.2.1. Simple Nearest Neighbor Classifier
    • 6.2.2. k-Nearest Neighbor Classifier
    • 6.2.3. Weighted Nearest Neighbor Classification
  • 6.3. Logistic Regression
    • 6.3.1. Logistic Regression Model
    • 6.3.2. Maximum Likelihood Estimator
    • 6.3.3. Gradient Descent
    • 6.3.4. Vectorized Logistic Regression Algorithm
    • 6.3.5. Extended Features in Logistic Regression
    • 6.3.6. Multi Class Logistic Regression
      • 6.3.6.1. One vs All Multi Class
      • 6.3.6.2. One vs One Multi Class
      • 6.3.6.3. Softmax Multi Class
  • 6.4. Neural Networks
    • 6.4.1. Neural Networks: the Third Revolution
    • 6.4.2. The Classical View on Neural Networks
    • 6.4.3. A Modern View On Neural Networks
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© Copyright 2021, Rein van den Boomgaard.

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