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Module Details

Course : Pattern Recognition

Subject : Computer science

No. of Modules : 130

Level : FACULTY,PG,STUDENTS,UG

Source : SwayamPrabha;Channel-13

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Sr. No. Title Creator/Author E-Text Video URL Metadata
1 Examples of uses or application of pattern recognition; and when to do clustering Prof. C.A.Murthy - - Click Here
2 Fcm and soft-computing techniques Prof. C.A.Murthy - - Click Here
3 Examples of real-life dataset Prof. C.A.Murthy - - Click Here
4 Support vector machine[svm] Prof. C.A.Murthy - - Click Here
5 Probability density estimation Prof. C.A.Murthy - - Click Here
6 Visualization and aggregation Prof. C.A.Murthy - - Click Here
7 Data condensation, feature clustering, data visualization Prof. C.A.Murthy - - Click Here
8 Basics of statistics, covariance and their properties Prof. C.A.Murthy - - Click Here
9 Comparison between performance of classifiers Prof. C.A.Murthy - - Click Here
10 Feature selection criteria function: interclass distance based Prof. C.A.Murthy - - Click Here
11 Principal components Prof. C.A.Murthy - - Click Here
12 Feature selection criteria function: probabilistic separability based Prof. C.A.Murthy - - Click Here
13 Cauchy schwartz inequality Prof. C.A.Murthy - - Click Here
14 Feature selection: sequential forward and backward selection Prof. C.A.Murthy - - Click Here
15 Feature selection: branch and bound algorithm Prof. C.A.Murthy - - Click Here
16 Feature selection: problem statement and uses Prof. C.A.Murthy - - Click Here
17 K-means algorithm and hierarchical clustering Prof. C.A.Murthy - - Click Here
18 K-medoids and dbscan Prof. C.A.Murthy - - Click Here
19 Basics of clustering, similarity/dissimilarity measures, clustering criteria Prof. C.A.Murthy - - Click Here
20 Gaussian mixture model (gmm) Prof. Sukhendu Das - - Click Here
21 Assignments Prof. C.A.Murthy - - Click Here
22 Principal component analysis (pca) Prof. Sukhendu Das - - Click Here
23 Fisher's lda Prof. Sukhendu Das - - Click Here
24 Linear and non-linear decision boundaries Prof. Sukhendu Das - - Click Here
25 K-nn classifier Prof. C.A.Murthy - - Click Here
26 Linear discriminant function and perceptron Prof. Sukhendu Das - - Click Here
27 Perceptron learning and decision boundaries Prof. Sukhendu Das - - Click Here
28 Bayes theorem Prof. Sukhendu Das - - Click Here
29 Normal distribution and decision boundaries ii Prof. Sukhendu Das - - Click Here
30 Normal distribution and decision boundaries i Prof. C.A. Murthy - - Click Here
31 Standardization, normalization, clustering and metric space Prof. C.A. Murthy - - Click Here
32 Training set, test set Prof. C.A. Murthy - - Click Here
33 Normal distribution and parameter estimation Prof. C.A. Murthy - - Click Here
34 Examples of bayes decision rule Prof. C.A. Murthy - - Click Here
35 Rank of matrix and svd Prof. Sukhendu Das - - Click Here
36 Types of errors Prof. C.A. Murthy - - Click Here
37 Vector spaces Prof. Sukhendu Das - - Click Here
38 Eigen value and eigen vectors Prof. Sukhendu Das - - Click Here
39 Relevant basics of linear algebra, vector spaces Prof. Sukhendu Das - - Click Here
40 Clustering vs. classification Prof. Sukhendu Das - - Click Here
41 Principles of pattern recognition iii (classification and bayes decision rule) Prof. C.A. Murthy - - Click Here
42 Principles of pattern recognition ii (mathematics) Prof. C.A. Murthy - - Click Here
43 Principles of pattern recognition i (introduction and uses) Prof. C.A. Murthy - - Click Here
44 Overview of pattern classifiers Prof. P. S. Sastry - - Click Here
45 Introduction to statistical pattern recognition Prof. P. S. Sastry - - Click Here
46 Principles of pattern recognition iii (classification and bayes decision rule) Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
47 Principles of pattern recognition i (introduction and uses) Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
48 Relevant basics of linear algebra, vector spaces Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
49 Principles of pattern recognition ii (mathematics) Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
50 Clustering vs. classification Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
51 Normal distribution and decision boundaries ii Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
52 Perceptron learning and decision boundaries Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
53 Linear discriminant function and perceptron Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
54 Linear and non-linear decision boundaries Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
55 Principal component analysis (pca) Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
56 Gaussian mixture model (gmm) Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
57 Fisherâ‚ã„ã´s lda Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
58 K-nn classifier Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
59 Bayes theorem Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
60 Feature selection criteria function: interclass distance based Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
61 Data condensation, feature clustering, data visualization Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
62 Basics of statistics, covariance, and their properties Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
63 Comparison between performance of classifiers Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
64 Fcm and soft-computing techniques Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
65 Probability density estimation Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
66 Visualization and aggregation Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
67 Support vector machine (svm) Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
68 Principal components Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
69 Examples of uses or application of pattern recognition; and when to do clustering Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
70 Examples of real-life dataset Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
71 Standardization, normalization, clustering and metric space Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
72 Normal distribution and decision boundaries i Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
73 Normal distribution and parameter estimation Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
74 Examples of bayes decision rule Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
75 Eigen value and eigen vectors Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
76 Rank of matrix and svd Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
77 Training set, test set Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
78 Types of errors Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
79 Vector spaces Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
80 Basics of clustering, similarity/dissimilarity measures, clustering criteria. Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
81 Feature selection criteria function: probabilistic separability based Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
82 Feature selection : sequential forward and backward selection Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
83 Feature selection : branch and bound algorithm Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
84 K-means algorithm and hierarchical clustering Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
85 Feature selection : problem statement and uses Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
86 Cauchy schwartz inequality Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
87 K-medoids and dbscan Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
88 Assignments Prof. Sukhendu Das,Prof. C.A. Murthy - - Click Here
89 Risk minimization view of adaboost Prof. P.S. Sastry - - Click Here
90 Overview of smo and other algorithms for svm; ?-svm and ?-svr; svm as a risk minimizer Prof. P.S. Sastry - - Click Here
91 No free lunch theorem; model selection and model estimation; bias-variance trade-off Prof. P.S. Sastry - - Click Here
92 Support vector regression and ?-insensitive loss function, examples of svm learning Prof. P.S. Sastry - - Click Here
93 Feature selection and dimensionality reduction; principal component analysis Prof. P.S. Sastry - - Click Here
94 Kernel functions for nonlinear svms; mercer and positive definite kernels Prof. P.S. Sastry - - Click Here
95 Bootstrap, bagging and boosting; classifier ensembles; adaboost Prof. P.S. Sastry - - Click Here
96 Svm formulation with slack variables; nonlinear svm classifiers Prof. P.S. Sastry - - Click Here
97 Positive definite kernels; rkhs; representer theorem Prof. P.S. Sastry - - Click Here
98 Assessing learnt classifiers; cross validation; Prof. P.S. Sastry - - Click Here
99 Feedforward networks for classification and regression; backpropagation in practice Prof. P.S. Sastry - - Click Here
100 Backpropagation algorithm; representational abilities of feedforward networks Prof. P.S. Sastry - - Click Here
101 Multilayer feedforward neural networks with sigmoidal activation functions; Prof. P.S. Sastry - - Click Here
102 Support vector machines -- introduction, obtaining the optimal hyperplane Prof. P.S. Sastry - - Click Here
103 Learning weights in rbf networks; k-means clustering algorithm Prof. P.S. Sastry - - Click Here
104 Radial basis function networks; gaussian rbf networks Prof. P.S. Sastry - - Click Here
105 Complexity of learning problems and vc-dimension Prof. P.S. Sastry - - Click Here
106 Vc-dimension examples; vc-dimension of hyperplanes Prof. P.S. Sastry - - Click Here
107 Overview of artificial neural networks Prof. P.S. Sastry - - Click Here
108 Linear discriminant functions for multi-class case; multi-class logistic regression Prof. P.S. Sastry - - Click Here
109 Logistic regression; statistics of least squares method; regularized least squares Prof. P.S. Sastry - - Click Here
110 Adaline and lms algorithm; general nonliner least-squares regression Prof. P.S. Sastry - - Click Here
111 Overview of statistical learning theory; empirical risk minimization Prof. P.S. Sastry - - Click Here
112 Consistency of empirical risk minimization; vc-dimension Prof. P.S. Sastry - - Click Here
113 Linear least squares regression; lms algorithm Prof. P.S. Sastry - - Click Here
114 Learning and generalization; pac learning framework Prof. P.S. Sastry - - Click Here
115 Consistency of empirical risk minimization Prof. P.S. Sastry - - Click Here
116 Fisher linear discriminant Prof. P.S. Sastry - - Click Here
117 Linear discriminant functions; perceptron -- learning algorithm and convergence proof Prof. P.S. Sastry - - Click Here
118 Bayesian estimation examples; the exponential family of densities and ml estimates Prof. P.S. Sastry - - Click Here
119 Sufficient statistics; recursive formulation of ml and bayesian estimates Prof. P.S. Sastry - - Click Here
120 Convergence of em algorithm, overview of nonparametric density estimation Prof. P.S. Sastry - - Click Here
121 Convergence of em algorithm; overview of nonparametric density estimation Prof. P.S. Sastry - - Click Here
122 Bayesian estimation of parameters of density functions, map estimates Prof. P.S. Sastry - - Click Here
123 Nonparametric estimation, parzen windows, nearest neighbour methods Prof. P.S. Sastry - - Click Here
124 Maximum likelihood estimation of different densities Prof. P.S. Sastry - - Click Here
125 Mixture densities, ml estimation and em algorithm Prof. P.S. Sastry - - Click Here
126 Implementing bayes classifier; estimation of class conditional densities Prof. P.S. Sastry - - Click Here
127 Estimating bayes error; minimax and neymann-pearson classifiers Prof. P.S. Sastry - - Click Here
128 Introduction to statistical pattern recognition Prof. P.S. Sastry - - Click Here
129 The bayes classifier for minimizing risk Prof. P.S. Sastry - - Click Here
130 Overview of pattern classifiers Prof. P.S. Sastry - - Click Here