Pattern Recognition by C. A. Murthy & Sukhendu Das (IIT Madras)

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source: nptelhrd    2014年10月8日
Computer Science - Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das, Department of Computer Science and Engineering, IIT Madras. For more details on NPTEL visit http://nptel.ac.in

01 Principles of Pattern Recognition I (Introduction and Uses) 46:40
02 Principles of Pattern Recognition II (Mathematics) 48:09
03 Principles of Pattern Recognition III (Classification and Bayes Decision Rule) 38:07
04 Clustering vs. Classification 46:55
05 Relevant Basics of Linear Algebra, Vector Spaces 55:22
06 Eigen Value and Eigen Vectors 46:01
07 Vector Spaces 33:56
08 Rank of Matrix and SVD 34:37
09 Types of Errors 41:42
10 Examples of Bayes Decision Rule 1:17:42
11 Normal Distribution and Parameter Estimation 28:16
12 Training Set, Test Set 43:15
13 Standardization, Normalization, Clustering and Metric Space 54:33
14 Normal Distribution and Decision Boundaries I 1:02:51
15 Normal Distribution and Decision Boundaries II 46:29
17 Linear Discriminant Function and Perceptron 57:31
18 Perceptron Learning and Decision Boundaries 48:17
19 Linear and Non-Linear Decision Boundaries 52:01
20 K-NN Classifier 53:48
21 Principal Component Analysis (PCA) 1:03:24
22 Fisher’s LDA 40:30
23 Gaussian Mixture Model (GMM) 26:06
24 Assignments 35:44
25 Basics of Clustering, Similarity/Dissimilarity Measures, Clustering Criteria. 33:14
26 K-Means Algorithm and Hierarchical Clustering.. 48:15
27 K-Medoids and DBSCAN 39:56
28 Feature Selection : Problem statement and Uses 49:46
29 Feature Selection : Branch and Bound Algorithm 53:14
30 Feature Selection : Sequential Forward and Backward Selection 46:58
16 Bayes Theorem 32:53
31 Cauchy Schwartz Inequality 27:56
32 Feature Selection Criteria Function: Probabilistic Separability Based 45:33
33 Feature Selection Criteria Function: Interclass Distance Based 47:06
34 Principal Components 50:48
35 Comparison Between Performance of Classifiers 33:47
36 Basics of Statistics, Covariance, and their Properties 28:29
37 Data Condensation, Feature Clustering, Data Visualization 54:38
38 Probability Density Estimation 49:34
39 Visualization and Aggregation 25:45
40 Support Vector Machine (SVM) 1:04:49
41 FCM and Soft-Computing Techniques 57:23
42 Examples of Uses or Application of Pattern Recognition; And When to do clustering 20:07
43 Examples of Real-Life Dataset 34:37