Learn · Pattern Recognition and Application
Lecture 6 of 30Week 3
Lecture 06 : Normal Density and Discriminant Function - I
Prof. Prabir Kumar Biswas · IIT Kharagpur
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All the lectures
- Lecture 01 : Introduction
- Lecture 02 : Feature Extraction - I
- Lecture 03 : Feature Extraction - II
- Lecture 04 : Bayes Decision Theory - I
- Lecture 05 : Bayes Decision Theory - II
- Lecture 06 : Normal Density and Discriminant Function - I
- Lecture 07 : Normal Density and Discriminant Function - II
- Lecture 08 : Bayes Decision Theory - Binary Features
- Lecture 09 : Maximum Likelihood Estimation
- Lecture 10 : Probability Density Estimation - I
- Lecture 11 : Probability Density Estimation - II
- Lecture 12 : Probability Density Estimation - III
- Lecture 13 : Probability Density Estimation - IV
- Lecture 14 : Dimensionality Problem
- Lecture 15 : Multiple Discriminant Analysis
- Lecture 16 : Principal Component Analysis - Tutorial
- Lecture 17 : Multiple Discriminant Analysis - Tutorial
- Lecture 18 : Perceptron Criteria - I
- Lecture 19 : Perceptron Criteria - II
- Lecture 20 : MSE Criteria
- Lecture 21 : Linear Discriminator Tutorial
- Lecture 22 : Neural Network - I
- Lecture 23 : Neural Network - II
- Lecture 24 : Neural Network -III/ Hopfield Network
- Lecture 25 : RBF Neural Network - I
- Lecture 26 : RBF Neural Network - II
- Lecture 27 : Support Vector Machine
- Lecture 28 : Clustering -I
- Lecture 29 : Clustering -II
- Lecture 30 : Clustering -III