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Last updated:

September 26, 2023

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FREE

This course includes:

Unlimited Duration

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Description

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)

Course Curriculum

    • Lecture 01 – Principles of Pattern Recognition I: Introduction and Uses Unlimited
    • Lecture 02 – Principles of Pattern Recognition II: Mathematics Unlimited
    • Lecture 03 – Principles of Pattern Recognition III: Classification and Bayes Decision Rule Unlimited
    • Lecture 04 – Clustering vs Classification Unlimited
    • Lecture 05 – Relevant Basics of Linear Algebra, Vector Spaces Unlimited
    • Lecture 06 – Eigenvalue and Eigenvectors Unlimited
    • Lecture 07 – Vector Spaces Unlimited
    • Lecture 08 – Rank of Matrix and SVD Unlimited
    • Lecture 09 – Types of Errors Unlimited
    • Lecture 10 – Examples of Bayes Decision Rule Unlimited
    • Lecture 11 – Normal Distribution and Parameter Estimation Unlimited
    • Lecture 12 – Training Set, Test Set Unlimited
    • Lecture 13 – Standardization, Normalization, Clustering and Metric Space Unlimited
    • Lecture 14 – Normal Distribution and Decision Boundaries I Unlimited
    • Lecture 15 – Normal Distribution and Decision Boundaries II Unlimited
    • Lecture 16 – Bayes Theorem Unlimited
    • Lecture 17 – Linear Discriminant Function and Perceptron Unlimited
    • Lecture 18 – Perceptron Learning and Decision Boundaries Unlimited
    • Lecture 19 – Linear and Nonlinear Decision Boundaries Unlimited
    • Lecture 20 – K-NN Classifier Unlimited
    • Lecture 21 – Principal Component Analysis (PCA) Unlimited
    • Lecture 22 – Fisher’s Linear Discriminant Analysis (LDA) Unlimited
    • Lecture 23 – Gaussian Mixture Model (GMM) Unlimited
    • Lecture 24 – Assignments Unlimited
    • Lecture 25 – Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria Unlimited
    • Lecture 26 – K-Means Algorithm and Hierarchical Clustering Unlimited
    • Lecture 27 – K-Medoids and DBSCAN Unlimited
    • Lecture 28 – Feature Selection: Problem Statement and Uses Unlimited
    • Lecture 29 – Feature Selection: Branch and Bound Algorithm Unlimited
    • Lecture 30 – Feature Selection: Sequential Forward and Backward Selection Unlimited
    • Lecture 31 – Cauchy-Schwarz Inequality Unlimited
    • Lecture 32 – Feature Selection Criteria Function: Probabilistic Separability Based Unlimited
    • Lecture 33 – Feature Selection Criteria Function: Interclass Distance Based Unlimited
    • Lecture 34 – Principal Components Unlimited
    • Lecture 35 – Comparison between Performance of Classifiers Unlimited
    • Lecture 36 – Basics of Statistics, Covariance, and their Properties Unlimited
    • Lecture 37 – Data Condensation, Feature Clustering, Data Visualization Unlimited
    • Lecture 38 – Probability Density Estimation Unlimited
    • Lecture 39 – Visualization and Aggregation Unlimited
    • Lecture 40 – Support Vector Machine (SVM) Unlimited
    • Lecture 41 – FCM and Soft-Computing Techniques Unlimited
    • Lecture 42 – Examples of Uses or Application of Pattern Recognition Unlimited
    • Lecture 43 – Examples of Real-Life Dataset Unlimited

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