Computer Science – Pattern Recognition

Mod-01 Lec-01 Principles of Pattern Recognition I (Introduction and Uses)

Mod-01 Lec-02 Principles of Pattern Recognition II (Mathematics)

Mod-01 Lec-03 Principles of Pattern Recognition III (Classification and Bayes Decision Rule)

Mod-01 Lec-04 Clustering vs. Classification

Mod-01 Lec-05 Relevant Basics of Linear Algebra, Vector Spaces

Mod-01 Lec-06 Eigen Value and Eigen Vectors

Mod-01 Lec-07 Vector Spaces

Mod-01 Lec-08 Rank of Matrix and SVD

Mod-02 Lec-09 Types of Errors

Mod-02 Lec-10 Examples of Bayes Decision Rule

Mod-02 Lec-11 Normal Distribution and Parameter Estimation

Mod-02 Lec-12 Training Set, Test Set

Mod-02 Lec-13 Standardization, Normalization, Clustering and Metric Space

Mod-02 Lec-14 Normal Distribution and Decision Boundaries I

Mod-02 Lec-15 Normal Distribution and Decision Boundaries II

Mod-02 Lec-16 Bayes Theorem

Mod-02 Lec-17 Linear Discriminant Function and Perceptron

Mod-02 Lec-18 Perceptron Learning and Decision Boundaries

Mod-02 Lec-19 Linear and Non-Linear Decision Boundaries

Mod-02 Lec-20 K-NN Classifier

Mod-02 Lec-21 Principal Component Analysis (PCA)

Mod-02 Lec-22 Fisher’s LDA

Mod-02 Lec-23 Gaussian Mixture Model (GMM)

Mod-02 Lec-24 Assignments

Mod-03 Lec-25 Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.

Mod-03 Lec-26 K-Means Algorithm and Hierarchical Clustering..

Mod-03 Lec-27 K-Medoids and DBSCAN

Mod-04 Lec-28 Feature Selection : Problem statement and Uses

Mod-04 Lec-29 Feature Selection : Branch and Bound Algorithm

Mod-04 Lec-30 Feature Selection : Sequential Forward and Backward Selection

Mod-04 Lec-31 Cauchy Schwartz Inequality

Mod-04 Lec-32 Feature Selection Criteria Function: Probabilistic Separability Based

Mod-04 Lec-33 Feature Selection Criteria Function: Interclass Distance Based

Mod-05 Lec-34 Principal Components

Mod-06 Lec-35 Comparison Between Performance of Classifiers

Mod-06 Lec-36 Basics of Statistics, Covariance, and their Properties

Mod-06 Lec-37 Data Condensation, Feature Clustering, Data Visualization

Mod-06 Lec-38 Probability Density Estimation

Mod-06 Lec-39 Visualization and Aggregation

Mod-06 Lec-40 Support Vector Machine (SVM)

Mod-06 Lec-41 FCM and Soft-Computing Techniques

Mod-06 Lec-43 Examples of Real-Life Dataset