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Pattern Recognition

Pattern Recognition

Validity Unlimited Pattern Recognition All Level Pattern Recognition English

0.0 (0)
Free

About This Course

Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases, and is often used interchangeably with these terms.

  • Pattern Recognition Total 6 Modules
  • Pattern Recognition 30 Videos
  • Pattern Recognition Downloadable on mobile
  • Pattern Recognition Published on 29 June, 2019

Course Curriculum

Classification

Pattern Recognition
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    Assignments

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    Gaussian Mixture Model (GMM)

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    Assignments

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Clustering

Pattern Recognition
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    Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.?

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    K-Means Algorithm and Hierarchical Clustering?

    39m <?php echo  $alt; ?>
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    K-Medoids and DBSCAN

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Feature Selection

Pattern Recognition
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    Feature Selection : Problem statement and Uses

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    Feature Selection : Branch and Bound Algorithm

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    Feature Selection : Sequential Forward and Backward Selection

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    Cauchy Schwartz Inequality

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    Feature Selection Criteria Function: Probabilistic Separability Based

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    Feature Selection Criteria Function: Interclass Distance Based

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Feature Extraction

Pattern Recognition
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    Principal Components

    50m <?php echo  $alt; ?>

Introduction and mathematical Preliminaries

Pattern Recognition
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    Vector Spaces

    33m <?php echo  $alt; ?>
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    Eigen Value and Eigen Vectors

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    Rank of Matrix and SVD

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    Relevant Basics of Linear Algebra, Vector Spaces

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    Clustering vs. Classification

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    Principles of Pattern Recognition II (Mathematics)

    34m <?php echo  $alt; ?>
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    Principles of Pattern Recognition III (Classification and Bayes Decision Rule)

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    Principles of Pattern Recognition I (Introduction and Uses)

    34m <?php echo  $alt; ?>

Recent Advances in pattern Recognition

Pattern Recognition
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    Comparison Between Performance of Classifiers

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    Examples of Real-Life Dataset

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    Examples of Uses or Application of Pattern Recognition; And When to do clustering

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    FCM and Soft-Computing Techniques?

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    Support Vector Machine (SVM)

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    Visualization and Aggregation

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    Probability Density Estimation

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    Data Condensation, Feature Clustering, Data Visualization

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    Basics of Statistics, Covariance, and their Properties

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