EE-Learning
Principal Component Analysis (PCA)
Catogry:
Computing
Subject:
Computer Science
Course:
Pattern Recognition I
Lecture List
Examples of Real-Life Dataset
Examples of Uses or Application of Pattern Recognition; And When to do clustering
FCM and Soft-Computing Techniques
Support Vector Machine (SVM)
Visualization and Aggregation
Probability Density Estimation
Data Condensation, Feature Clustering, Data Visualization
Basics of Statistics, Covariance, and their Properties
Comparison Between Performance of Classifiers
Principal Components
Feature Selection Criteria Function: Interclass Distance Based
Feature Selection Criteria Function: Probabilistic Separability Based
Cauchy Schwartz Inequality
Bayes Theorem
Feature Selection : Sequential Forward and Backward Selection
Feature Selection : Branch and Bound Algorithm
Feature Selection : Problem statement and Uses
K-Medoids and DBSCAN
K-Means Algorithm and Hierarchical Clustering..
Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.
Assignments
Gaussian Mixture Model (GMM)
Fisher’s LDA
Principal Component Analysis (PCA)
K-NN Classifier
Linear and Non-Linear Decision Boundaries
Perceptron Learning and Decision Boundaries
Linear Discriminant Function and Perceptron
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Normal Distribution and Decision Boundaries II
Normal Distribution and Decision Boundaries I
Standardization, Normalization, Clustering and Metric Space
Training Set, Test Set
Normal Distribution and Parameter Estimation
Examples of Bayes Decision Rule
Types of Errors
Rank of Matrix and SVD
Vector Spaces
Eigen Value and Eigen Vectors
Relevant Basics of Linear Algebra, Vector Spaces
Clustering vs. Classification
Principles of Pattern Recognition III (Classification and Bayes Decision Rule)
Principles of Pattern Recognition II (Mathematics)
Principles of Pattern Recognition I (Introduction and Uses)