| 1 | Principles of Pattern Recognition I (Introduction and Uses) | PDF unavailable |
| 2 | Principles of Pattern Recognition II (Mathematics) | PDF unavailable |
| 3 | Principles of Pattern Recognition III (Classification and Bayes Decision Rule) | PDF unavailable |
| 4 | Clustering vs. Classification | PDF unavailable |
| 5 | Relevant Basics of Linear Algebra, Vector Spaces | PDF unavailable |
| 6 | Eigen Value and Eigen Vectors | PDF unavailable |
| 7 | Vector Spaces | PDF unavailable |
| 8 | Rank of Matrix and SVD | PDF unavailable |
| 9 | Types of Errors | PDF unavailable |
| 10 | Examples of Bayes Decision Rule | PDF unavailable |
| 11 | Normal Distribution and Parameter Estimation | PDF unavailable |
| 12 | Training Set, Test Set | PDF unavailable |
| 13 | Standardization, Normalization, Clustering and Metric Space | PDF unavailable |
| 14 | Normal Distribution and Decision Boundaries I | PDF unavailable |
| 15 | Normal Distribution and Decision Boundaries II | PDF unavailable |
| 16 | Bayes Theorem | PDF unavailable |
| 17 | Linear Discriminant Function and Perceptron | PDF unavailable |
| 18 | Perceptron Learning and Decision Boundaries | PDF unavailable |
| 19 | Linear and Non-Linear Decision Boundaries | PDF unavailable |
| 20 | K-NN Classifier | PDF unavailable |
| 21 | Principal Component Analysis (PCA) | PDF unavailable |
| 22 | Fisher’s LDA | PDF unavailable |
| 23 | Gaussian Mixture Model (GMM) | PDF unavailable |
| 24 | Assignments | PDF unavailable |
| 25 | Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria. | PDF unavailable |
| 26 | K-Means Algorithm and Hierarchical Clustering | PDF unavailable |
| 27 | K-Medoids and DBSCAN | PDF unavailable |
| 28 | Feature Selection : Problem statement and Uses | PDF unavailable |
| 29 | Feature Selection : Branch and Bound Algorithm | PDF unavailable |
| 30 | Feature Selection : Sequential Forward and Backward Selection | PDF unavailable |
| 31 | Cauchy Schwartz Inequality | PDF unavailable |
| 32 | Feature Selection Criteria Function: Probabilistic Separability Based | PDF unavailable |
| 33 | Feature Selection Criteria Function: Interclass Distance Based | PDF unavailable |
| 34 | Principal Components | PDF unavailable |
| 35 | Comparison Between Performance of Classifiers | PDF unavailable |
| 36 | Basics of Statistics, Covariance, and their Properties | PDF unavailable |
| 37 | Data Condensation, Feature Clustering, Data Visualization | PDF unavailable |
| 38 | Probability Density Estimation | PDF unavailable |
| 39 | Visualization and Aggregation | PDF unavailable |
| 40 | Support Vector Machine (SVM) | PDF unavailable |
| 41 | FCM and Soft-Computing Techniques | PDF unavailable |
| 42 | Examples of Uses or Application of Pattern Recognition; And When to do clustering | PDF unavailable |
| 43 | Examples of Real-Life Dataset | PDF unavailable |