Pattern Recognition Video Lectures

Pattern Recognition
'Pattern Recognition' Video Lectures by Prof. Sukhendu Das, Prof. C.A. Murthy from IIT Madras
"Pattern Recognition" - Video Lectures
1. Principles of Pattern Recognition I (Introduction and Uses)
2. Principles of Pattern Recognition II (Mathematics)
3. Principles of Pattern Recognition III (Classification and Bayes Decision Rule)
4. Clustering vs. Classification
5. Relevant Basics of Linear Algebra, Vector Spaces
6. Eigen Value and Eigen Vectors
7. Vector Spaces
8. Rank of Matrix and SVD
9. Types of Errors
10. Examples of Bayes Decision Rule
11. Normal Distribution and Parameter Estimation
12. Training Set, Test Set
13. Standardization, Normalization, Clustering and Metric Space
14. Normal Distribution and Decision Boundaries I
15. Normal Distribution and Decision Boundaries II
16. Bayes Theorem
17. Linear Discriminant Function and Perceptron
18. Perceptron Learning and Decision Boundaries
19. Linear and Non-Linear Decision Boundaries
20. K-NN Classifier
21. Principal Component Analysis (PCA)
22. Fisher’s LDA
23. Gaussian Mixture Model (GMM)
24. Assignments
25. Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria.
26. K-Means Algorithm and Hierarchical Clustering
27. K-Medoids and DBSCAN
28. Feature Selection : Problem statement and Uses
29. Feature Selection : Branch and Bound Algorithm
30. Feature Selection : Sequential Forward and Backward Selection
31. Cauchy Schwartz Inequality
32. Feature Selection Criteria Function: Probabilistic Separability Based
33. Feature Selection Criteria Function: Interclass Distance Based
34. Principal Components
35. Comparison Between Performance of Classifiers
36. Basics of Statistics, Covariance, and their Properties
37. Data Condensation, Feature Clustering, Data Visualization
38. Probability Density Estimation
39. Visualization and Aggregation
40. Support Vector Machine (SVM)
41. FCM and Soft-Computing Techniques
42. Examples of Uses or Application of Pattern Recognition; And When to do clustering
43. Examples of Real-Life Dataset
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