Ashuthosh Shridhar ~

Pattern Recognition

Nov 02, 20241 min read

I. Bayesian Decision Theory

Fundamentals

  1. Posterior, Likelihood, Prior and Evidence
  2. Bayes Theorem

Bayesian Classification

  1. Bayes Classifier
  2. Bayes Decision Rule or Maximum A Posteriori
  3. Reconstructing Evidence
  4. Classifier with the Lowest Error Probability
  5. Error Rate comparison between a Random Guess and a Bayesian Guess
  1. Maximum Likelihood Decision Rule
  2. Likelihood Ratio
  3. Maximum Likelihood vs Maximum A Posteriori in action

Error and Risk Analysis

  1. Probability Error
  2. Bayes Risk

Discriminant Functions

  1. Discriminant Function
  2. Equivalent Discriminant Function and Examples

Bayesian Networks

  1. Bayesian Belief Networks
  2. Computation of Variables in a Bayesian Belief Network

II. Quality Measures and Evaluation Metrics

Classification Metrics

  1. Binary Classifications
  2. Precision, Accuracy & Error Rate
  3. Intersection over Union (IoU)

Performance Evaluation

  1. Receiving Operator Characteristics
  2. Equal Error Rate

III. Supervised Estimation of Distribution Parameters

Probabilistic Models

  1. Multivariate Gaussian Model and Gaussian Mixture Model

Parameter Estimation Techniques

  1. Parameter Estimation for Maximum Likelihood
  2. Expectation Maximization Algorithm

IV. Neural Networks

Activation Functions

  1. Activation Functions
  2. Activation Function for a Bayesian Classifier

Graph View

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