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