Pattern Recognition I

Lecture Details Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit httpnptel.ac.in

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Last updated Fri, 17-Jun-2022
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Course overview
Overview

Introduction and mathematical preliminaries:What is Pattern recognition; Applications and Examples - Clustering vs. Classification; Supervised vs. unsupervised - Relevant basics of Linear Algebra, vector spaces - Probability Theory basics - Basics of Estimation theory - Decision Boundaries, Decision region / Metric spaces/ distances - Mathematical Assignments;Classification:Bayes decision rule, Error probability - Examples - Normal Distribution - Linear Discriminant Function (equal covariance matrices) - Non-linear Decision Boundaries (unequal covariance matrices) - Mahalanobis Distance - K-NN Classifier - Fishers LDA - Single Layer Perceptron - Multi-layer Perceptron - Training set, test set; standardization and normalization;Clustering:Basics of Clustering; similarity / dissimilarity measures; clustering criteria - Different distance functions and similarity measures - Minimum within cluster distance criterion - K-means algorithm;Single linkage and complete linkage algorithms, MST - K-medoids, DBSCAN - Data sets - Visualization; Unique Clustering; No existence of clusters;Feature selection:Problem statement and Uses; Algorithms - Branch and bound algorithm, sequential forward / backward selection algorithms, (l,r) algorithm;Probabilistic separability based criterion functions, interclass distance based criterion functions;Feature Extraction:PCA + Kernel PCA;Recent advances in Pattern Recognition:Structural PR, SVMs, FCM, Soft-computing and Neuro-fuzzy techniques, and real-life examples

Curriculum for this course
43 Lessons 32:06:15 Hours
Lecture
43 Lessons 32:06:15 Hours
  • Principles of Pattern Recognition I (Introduction and Uses)
    Preview 00:46:38
  • Principles of Pattern Recognition II (Mathematics)
    00:48:08
  • Principles of Pattern Recognition III (Classification and Bayes Decision Rule)
    00:38:06
  • Clustering vs. Classification
    00:46:54
  • Relevant Basics of Linear Algebra, Vector Spaces
    00:55:24
  • Eigen Value and Eigen Vectors
    00:46:04
  • Vector Spaces
    00:33:56
  • Rank of Matrix and SVD
    00:34:36
  • Types of Errors
    00:41:42
  • Examples of Bayes Decision Rule
    01:17:41
  • Normal Distribution and Parameter Estimation
    00:28:15
  • Training Set, Test Set
    00:43:14
  • Standardization, Normalization, Clustering and Metric Space
    00:54:32
  • Normal Distribution and Decision Boundaries I
    01:02:51
  • Normal Distribution and Decision Boundaries II
    00:46:28
  • Linear Discriminant Function and Perceptron
    00:57:32
  • Perceptron Learning and Decision Boundaries
    00:48:16
  • Linear and Non-Linear Decision Boundaries
    00:52:01
  • K-NN Classifier
    00:53:47
  • Principal Component Analysis (PCA)
    01:03:23
  • Fisher’s LDA
    00:40:29
  • Gaussian Mixture Model (GMM)
    00:26:05
  • Assignments
    00:35:43
  • Basics of Clustering, SimilarityDissimilarity Measures, Clustering Criteria.
    00:33:14
  • K-Means Algorithm and Hierarchical Clustering..
    00:48:15
  • K-Medoids and DBSCAN
    00:39:55
  • Feature Selection Problem statement and Uses
    00:49:44
  • Feature Selection Branch and Bound Algorithm
    00:53:13
  • Feature Selection Sequential Forward and Backward Selection
    00:46:57
  • Bayes Theorem
    00:32:52
  • Cauchy Schwartz Inequality
    00:27:56
  • Feature Selection Criteria Function Probabilistic Separability Based
    00:45:32
  • Feature Selection Criteria Function Interclass Distance Based
    00:47:05
  • Principal Components
    00:50:47
  • Comparison Between Performance of Classifiers
    00:33:46
  • Basics of Statistics, Covariance, and their Properties
    00:28:28
  • Data Condensation, Feature Clustering, Data Visualization
    00:54:37
  • Probability Density Estimation
    00:49:33
  • Visualization and Aggregation
    00:25:44
  • Support Vector Machine (SVM)
    01:04:48
  • FCM and Soft-Computing Techniques
    00:57:22
  • Examples of Uses or Application of Pattern Recognition; And When to do clustering
    00:20:06
  • Examples of Real-Life Dataset
    00:34:36
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