Pattern Recognition


Lecture 1 - Principles of Pattern Recognition I (Introduction and Uses)


Lecture 2 - Principles of Pattern Recognition II (Mathematics)


Lecture 3 - Principles of Pattern Recognition III (Classification and Bayes Decision Rule)


Lecture 4 - Clustering vs. Classification


Lecture 5 - Relevant Basics of Linear Algebra, Vector Spaces


Lecture 6 - Eigen Value and Eigen Vectors


Lecture 7 - Vector Spaces


Lecture 8 - Rank of Matrix and SVD


Lecture 9 - Types of Errors


Lecture 10 - Examples of Bayes Decision Rule


Lecture 11 - Normal Distribution and Parameter Estimation


Lecture 12 - Training Set, Test Set


Lecture 13 - Standardization, Normalization, Clustering and Metric Space


Lecture 14 - Normal Distribution and Decision Boundaries I


Lecture 15 - Normal Distribution and Decision Boundaries II


Lecture 16 - Bayes Theorem


Lecture 17 - Linear Discriminant Function and Perceptron


Lecture 18 - Perceptron Learning and Decision Boundaries


Lecture 19 - Linear and Non-Linear Decision Boundaries


Lecture 20 - K-NN Classifier


Lecture 21 - Principal Component Analysis (PCA)


Lecture 22 - Fisher’s LDA


Lecture 23 - Gaussian Mixture Model (GMM)


Lecture 24 - Assignments


Lecture 25 - Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.


Lecture 26 - K-Means Algorithm and Hierarchical Clustering


Lecture 27 - K-Medoids and DBSCAN


Lecture 28 - Feature Selection : Problem statement and Uses


Lecture 29 - Feature Selection : Branch and Bound Algorithm


Lecture 30 - Feature Selection : Sequential Forward and Backward Selection


Lecture 31 - Cauchy Schwartz Inequality


Lecture 32 - Feature Selection Criteria Function: Probabilistic Separability Based


Lecture 33 - Feature Selection Criteria Function: Interclass Distance Based


Lecture 34 - Principal Components


Lecture 35 - Comparison Between Performance of Classifiers


Lecture 36 - Basics of Statistics, Covariance, and their Properties


Lecture 37 - Data Condensation, Feature Clustering, Data Visualization


Lecture 38 - Probability Density Estimation


Lecture 39 - Visualization and Aggregation


Lecture 40 - Support Vector Machine (SVM)


Lecture 41 - FCM and Soft-Computing Techniques


Lecture 42 - Examples of Uses or Application of Pattern Recognition; And When to do clustering


Lecture 43 - Examples of Real-Life Dataset