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
