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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)

Lecture 13 - Standardization, Normalization, Clustering and Metric Space

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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