Computer Science - Pattern Recognition (English)

   Thursday, April 24, 2025   

Starting a new Lecture Notes Series on Computer Science - Pattern Recognition

Youtube Lecture Playlist Credits 

Channel Name: nptelhrd

So Let Us Start to This Journey of Learning 

Computer Science - Pattern Recognition By Lecture Notes together!

Lecture 1: Mod-01 Lec-01 Principles of Pattern Recognition I (Introduction and Uses)

Lecture 2: Mod-01 Lec-02 Principles of Pattern Recognition II (Mathematics)

Lecture 3: Mod-01 Lec-03 Principles of Pattern Recognition III (Classification and Bayes Decision Rule)

Lecture 4: Mod-01 Lec-04 Clustering vs. Classification

Lecture 5: Mod-01 Lec-05 Relevant Basics of Linear Algebra, Vector Spaces

Lecture 6: Mod-01 Lec-06 Eigen Value and Eigen Vectors

Lecture 7: Mod-01 Lec-07 Vector Spaces

Lecture 8: Mod-01 Lec-08 Rank of Matrix and SVD

Lecture 9: Mod-02 Lec-09 Types of Errors

Lecture 10: Mod-02 Lec-10 Examples of Bayes Decision Rule

Lecture 11: Mod-02 Lec-11 Normal Distribution and Parameter Estimation

Lecture 12: Mod-02 Lec-12 Training Set, Test Set

Lecture 13: Mod-02 Lec-13 Standardization, Normalization, Clustering and Metric Space

Lecture 14: Mod-02 Lec-14 Normal Distribution and Decision Boundaries I

Lecture 15: Mod-02 Lec-15 Normal Distribution and Decision Boundaries II

Lecture 16: Mod-02 Lec-17 Linear Discriminant Function and Perceptron

Lecture 17: Mod-02 Lec-18 Perceptron Learning and Decision Boundaries

Lecture 18: Mod-02 Lec-19 Linear and Non-Linear Decision Boundaries

Lecture 19: Mod-02 Lec-20 K-NN Classifier

Lecture 20: Mod-02 Lec-21 Principal Component Analysis (PCA)

Lecture 21: Mod-02 Lec-22 Fisher’s LDA

Lecture 22: Mod-02 Lec-23 Gaussian Mixture Model (GMM)

Lecture 23: Mod-02 Lec-24 Assignments

Lecture 24: Mod-03 Lec-25 Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.

Lecture 25: Mod-03 Lec-26 K-Means Algorithm and Hierarchical Clustering..

Lecture 26: Mod-03 Lec-27 K-Medoids and DBSCAN

Lecture 27: Mod-04 Lec-28 Feature Selection : Problem statement and Uses

Lecture 28: Mod-04 Lec-29 Feature Selection : Branch and Bound Algorithm

Lecture 29: Mod-04 Lec-30 Feature Selection : Sequential Forward and Backward Selection

Lecture 30: Mod-02 Lec-16 Bayes Theorem

Lecture 31: Mod-04 Lec-31 Cauchy Schwartz Inequality

Lecture 32: Mod-04 Lec-32 Feature Selection Criteria Function: Probabilistic Separability Based

Lecture 33: Mod-04 Lec-33 Feature Selection Criteria Function: Interclass Distance Based

Lecture 34: Mod-05 Lec-34 Principal Components

Lecture 35: Mod-06 Lec-35 Comparison Between Performance of Classifiers

Lecture 36: Mod-06 Lec-36 Basics of Statistics, Covariance, and their Properties

Lecture 37: Mod-06 Lec-37 Data Condensation, Feature Clustering, Data Visualization

Lecture 38: Mod-06 Lec-38 Probability Density Estimation

Lecture 39: Mod-06 Lec-39 Visualization and Aggregation

Lecture 40: Mod-06 Lec-40 Support Vector Machine (SVM)

Lecture 41: Mod-06 Lec-41 FCM and Soft-Computing Techniques

Lecture 42: Mod-06 Lec-42 Examples of Uses or Application of Pattern Recognition; And When to do clustering

Lecture 43: Mod-06 Lec-43 Examples of Real-Life Dataset