Modules / Lectures


Sl.No Chapter Name MP4 Download
1Principles of Pattern Recognition I (Introduction and Uses)Download
2Principles of Pattern Recognition II (Mathematics)Download
3Principles of Pattern Recognition III (Classification and Bayes Decision Rule)Download
4Clustering vs. ClassificationDownload
5Relevant Basics of Linear Algebra, Vector SpacesDownload
6Eigen Value and Eigen VectorsDownload
7Vector SpacesDownload
8Rank of Matrix and SVDDownload
9Types of ErrorsDownload
10Examples of Bayes Decision RuleDownload
11Normal Distribution and Parameter EstimationDownload
12Training Set, Test SetDownload
13Standardization, Normalization, Clustering and Metric SpaceDownload
14Normal Distribution and Decision Boundaries IDownload
15Normal Distribution and Decision Boundaries IIDownload
16Bayes TheoremDownload
17Linear Discriminant Function and PerceptronDownload
18Perceptron Learning and Decision BoundariesDownload
19Linear and Non-Linear Decision BoundariesDownload
20K-NN Classifier Download
21Principal Component Analysis (PCA) Download
22Fisher’s LDADownload
23Gaussian Mixture Model (GMM)Download
24AssignmentsDownload
25Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria. Download
26K-Means Algorithm and Hierarchical Clustering Download
27K-Medoids and DBSCANDownload
28Feature Selection : Problem statement and UsesDownload
29Feature Selection : Branch and Bound AlgorithmDownload
30Feature Selection : Sequential Forward and Backward SelectionDownload
31Cauchy Schwartz InequalityDownload
32Feature Selection Criteria Function: Probabilistic Separability BasedDownload
33Feature Selection Criteria Function: Interclass Distance BasedDownload
34Principal ComponentsDownload
35Comparison Between Performance of ClassifiersDownload
36Basics of Statistics, Covariance, and their PropertiesDownload
37Data Condensation, Feature Clustering, Data VisualizationDownload
38Probability Density EstimationDownload
39Visualization and AggregationDownload
40Support Vector Machine (SVM)Download
41FCM and Soft-Computing Techniques Download
42Examples of Uses or Application of Pattern Recognition; And When to do clusteringDownload
43Examples of Real-Life DatasetDownload

Sl.No Chapter Name English
1Principles of Pattern Recognition I (Introduction and Uses)PDF unavailable
2Principles of Pattern Recognition II (Mathematics)PDF unavailable
3Principles of Pattern Recognition III (Classification and Bayes Decision Rule)PDF unavailable
4Clustering vs. ClassificationPDF unavailable
5Relevant Basics of Linear Algebra, Vector SpacesPDF unavailable
6Eigen Value and Eigen VectorsPDF unavailable
7Vector SpacesPDF unavailable
8Rank of Matrix and SVDPDF unavailable
9Types of ErrorsPDF unavailable
10Examples of Bayes Decision RulePDF unavailable
11Normal Distribution and Parameter EstimationPDF unavailable
12Training Set, Test SetPDF unavailable
13Standardization, Normalization, Clustering and Metric SpacePDF unavailable
14Normal Distribution and Decision Boundaries IPDF unavailable
15Normal Distribution and Decision Boundaries IIPDF unavailable
16Bayes TheoremPDF unavailable
17Linear Discriminant Function and PerceptronPDF unavailable
18Perceptron Learning and Decision BoundariesPDF unavailable
19Linear and Non-Linear Decision BoundariesPDF unavailable
20K-NN Classifier PDF unavailable
21Principal Component Analysis (PCA) PDF unavailable
22Fisher’s LDAPDF unavailable
23Gaussian Mixture Model (GMM)PDF unavailable
24AssignmentsPDF unavailable
25Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria. PDF unavailable
26K-Means Algorithm and Hierarchical Clustering PDF unavailable
27K-Medoids and DBSCANPDF unavailable
28Feature Selection : Problem statement and UsesPDF unavailable
29Feature Selection : Branch and Bound AlgorithmPDF unavailable
30Feature Selection : Sequential Forward and Backward SelectionPDF unavailable
31Cauchy Schwartz InequalityPDF unavailable
32Feature Selection Criteria Function: Probabilistic Separability BasedPDF unavailable
33Feature Selection Criteria Function: Interclass Distance BasedPDF unavailable
34Principal ComponentsPDF unavailable
35Comparison Between Performance of ClassifiersPDF unavailable
36Basics of Statistics, Covariance, and their PropertiesPDF unavailable
37Data Condensation, Feature Clustering, Data VisualizationPDF unavailable
38Probability Density EstimationPDF unavailable
39Visualization and AggregationPDF unavailable
40Support Vector Machine (SVM)PDF unavailable
41FCM and Soft-Computing Techniques PDF unavailable
42Examples of Uses or Application of Pattern Recognition; And When to do clusteringPDF unavailable
43Examples of Real-Life DatasetPDF unavailable


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available