Unit 1 Introduction

• Pattern Recognition Systems
• The Design Cycle
• Learning and Adaptation 02 5

Unit 2 Bayesian Decision Theory

8 lecture hours

• Bayesian Decision Theory-Continuous Features
• Minimum-Error-Rate Classification
• Discriminant Functions, and Decision Surfaces
• The Normal Density
• Discriminant Functions for the Normal Density
• Error Probabilities and Integrals
• Error Bounds for Normal Densities
• Bayes Decision Theory-Discrete Features
• Missing and Noisy Features
• Bayesian Belief Networks
• Subject Name : Pattern Recognition Subject Code : 1739503

Unit 3 Maximum-Likelihood and Bayesian Parameter Estimation

10 lecture hours

• Maximum- Likelihood Estimation
• Bayesian Estimation
• Bayesian Parameter Estimation General Case & General Theory
• Sufficient Statistics
• Problems of Dimensionality
• Component Analysis and Discriminants
• Expectation- Maximization (EM)

Unit 4 Linear Discriminant Functions

10 lecture hours

• Linear Discriminant Functions and Decision Surfaces
• Generalized Linear Discriminant Functions
• The Two- Category Linearly Separable Case
• Minimizing the Perceptron Criterion Function
• Relaxation Procedures
• Nonseparable Behavior
• Minimum Squared- Error Procedures
• The Ho-Kashyap Procedures
• Linear Programming Algorithms
• Support Vector Machines

Unit 5 Multilayer Neural Networks

12 lecture hours

• Feedforward Operation and Classification
• Backpropagation Algorithm
• Error Surfaces
• Backpropagation as Feature Mapping
• Bayes Theory and Probability
• Practical Techniques for Improving Backpropagation
• Second-Order Methods
• Additional Networks and Training Methods
• Complexity Adjustment and Pruning