Supervised Learning

  1. Probabilistic Models
    1. Naive Bayes Classifiers
      1. Bayes' Theorem
        1. Mathematical Foundation
          1. Prior and Posterior Probabilities
            1. Likelihood Function
            2. The Naive Independence Assumption
              1. Conditional Independence
                1. Implications and Limitations
                2. Types of Naive Bayes
                  1. Gaussian Naive Bayes
                    1. Continuous Features
                      1. Normal Distribution Assumption
                        1. Parameter Estimation
                        2. Multinomial Naive Bayes
                          1. Discrete Features
                            1. Text Classification Applications
                              1. Smoothing Techniques
                              2. Bernoulli Naive Bayes
                                1. Binary Features
                                  1. Document Classification
                                  2. Complement Naive Bayes
                                  3. Training Process
                                    1. Parameter Estimation
                                      1. Laplace Smoothing
                                        1. Handling Zero Probabilities
                                        2. Prediction Process
                                          1. Posterior Probability Calculation
                                            1. Classification Decision
                                            2. Applications and Limitations
                                              1. Text Classification
                                                1. Spam Detection
                                                  1. Medical Diagnosis
                                                    1. Feature Independence Violations

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                                                6. Support Vector Machines

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                                                8. Model Evaluation and Validation