NPTEL : Pattern Recognition (Computer Science and Engineering)

Co-ordinators : Prof. Sukhendu Das, Prof. C.A. Murthy


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