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Course10610604643 lectures

Pattern Recognition

Prof. Sukhendu Das and Prof. C.A. Murthy · IIT Madras

Start with lecture 1 At NPTEL

Lectures

Introduction and Mathematical Preliminaries

  1. Principles of Pattern Recognition I (Introduction and Uses) Video
  2. Principles of Pattern Recognition II (Mathematics) Video
  3. Principles of Pattern Recognition III (Classification and Bayes Decision Rule) Video
  4. Clustering vs. Classification Video
  5. Relevant Basics of Linear Algebra, Vector Spaces Video
  6. Eigen Value and Eigen Vectors Video
  7. Vector Spaces Video
  8. Rank of Matrix and SVD Video

Classification

  1. Types of Errors Video
  2. Examples of Bayes Decision Rule Video
  3. Normal Distribution and Parameter Estimation Video
  4. Training Set, Test Set Video
  5. Standardization, Normalization, Clustering and Metric Space Video
  6. Normal Distribution and Decision Boundaries I Video
  7. Normal Distribution and Decision Boundaries II Video
  8. Bayes Theorem Video
  9. Linear Discriminant Function and Perceptron Video
  10. Perceptron Learning and Decision Boundaries Video
  11. Linear and Non-Linear Decision Boundaries Video
  12. K-NN Classifier Video
  13. Principal Component Analysis (PCA) Video
  14. Fisher’s LDA Video
  15. Gaussian Mixture Model (GMM) Video
  16. Assignments Video

Clustering

  1. Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria. Video
  2. K-Means Algorithm and Hierarchical Clustering Video
  3. K-Medoids and DBSCAN Video

Feature Selection

  1. Feature Selection : Problem statement and Uses Video
  2. Feature Selection : Branch and Bound Algorithm Video
  3. Feature Selection : Sequential Forward and Backward Selection Video
  4. Cauchy Schwartz Inequality Video
  5. Feature Selection Criteria Function: Probabilistic Separability Based Video
  6. Feature Selection Criteria Function: Interclass Distance Based Video

Feature Extraction

  1. Principal Components Video

Recent Advances in Pattern Recognition

  1. Comparison Between Performance of Classifiers Video
  2. Basics of Statistics, Covariance, and their Properties Video
  3. Data Condensation, Feature Clustering, Data Visualization Video
  4. Probability Density Estimation Video
  5. Visualization and Aggregation Video
  6. Support Vector Machine (SVM) Video
  7. FCM and Soft-Computing Techniques Video
  8. Examples of Uses or Application of Pattern Recognition; And When to do clustering Video
  9. Examples of Real-Life Dataset Video

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Madras
Department
Computer Science and Engineering
Taught by
Prof. Sukhendu Das, Prof. C.A. Murthy
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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