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
VIDEO

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.