# InfoCoBuild

## Pattern Recognition

Pattern Recognition. Instructors: Prof. Sukhendu Das, Department of Computer Science and Engineering, IIT Madras; Prof. C. A. Murthy, ISI Kolkata. This course introduces the basic concepts and applications of pattern recognition. It covers lessons in linear algebra, probability theory, estimation techniques, classification, clustering, feature selection, feature extraction, and recent advances in pattern recognition. (from nptel.ac.in)

 Principles of Pattern Recognition

 Introduction and Mathematical Preliminaries Lecture 01 - Principles of Pattern Recognition I: Introduction and Uses Lecture 02 - Principles of Pattern Recognition II: Mathematics Lecture 03 - Principles of Pattern Recognition III: Classification and Bayes Decision Rule Lecture 04 - Clustering vs Classification Lecture 05 - Relevant Basics of Linear Algebra, Vector Spaces Lecture 06 - Eigenvalue and Eigenvectors Lecture 07 - Vector Spaces Lecture 08 - Rank of Matrix and SVD Classification Lecture 09 - 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 Nonlinear Decision Boundaries Lecture 20 - K-NN Classifier Lecture 21 - Principal Component Analysis (PCA) Lecture 22 - Fisher's Linear Discriminant Analysis (LDA) Lecture 23 - Gaussian Mixture Model (GMM) Lecture 24 - Assignments Clustering Lecture 25 - Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria Lecture 26 - K-Means Algorithm and Hierarchical Clustering Lecture 27 - K-Medoids and DBSCAN Feature Selection 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-Schwarz Inequality Lecture 32 - Feature Selection Criteria Function: Probabilistic Separability Based Lecture 33 - Feature Selection Criteria Function: Interclass Distance Based Feature Extraction Lecture 34 - Principal Components Recent Advances in Pattern Recognition 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 Lecture 43 - Examples of Real-Life Dataset

 References Pattern Recognition Instructors: Prof. Sukhendu Das, IIT Madras; Prof. C. A. Murthy, ISI Kolkata. This course introduces the basic concepts and applications of pattern recognition.