Pattern Recognition Video Lectures

Pattern Recognition
'Pattern Recognition' Video Lectures by Prof. P.S. Sastry from IISc Bangalore
"Pattern Recognition" - Video Lectures
1. Introduction to Statistical Pattern Recognition
2. Overview of Pattern Classifiers
3. The Bayes Classifier for minimizing Risk
4. Estimating Bayes Error; Minimax and Neymann-Pearson classifiers
5. Implementing Bayes Classifier; Estimation of Class Conditional Densities
6. Maximum Likelihood estimation of different densities
7. Bayesian estimation of parameters of density functions, MAP estimates
8. Bayesian Estimation examples; the exponential family of densities and ML estimates
9. Sufficient Statistics; Recursive formulation of ML and Bayesian estimates
10. Mixture Densities, ML estimation and EM algorithm
11. Convergence of EM algorithm; overview of Nonparametric density estimation
12. Convergence of EM algorithm, Overview of Nonparametric density estimation
13. Nonparametric estimation, Parzen Windows, nearest neighbour methods
14. Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof
15. Linear Least Squares Regression; LMS algorithm
16. AdaLinE and LMS algorithm; General nonliner least-squares regression
17. Logistic Regression; Statistics of least squares method; Regularized Least Squares
18. Fisher Linear Discriminant
19. Linear Discriminant functions for multi-class case; multi-class logistic regression
20. Learning and Generalization; PAC learning framework
21. Overview of Statistical Learning Theory; Empirical Risk Minimization
22. Consistency of Empirical Risk Minimization
23. Consistency of Empirical Risk Minimization; VC-Dimension
24. Complexity of Learning problems and VC-Dimension
25. VC-Dimension Examples; VC-Dimension of hyperplanes
26. Overview of Artificial Neural Networks
27. Multilayer Feedforward Neural networks with Sigmoidal activation functions;
28. Backpropagation Algorithm; Representational abilities of feedforward networks
29. Feedforward networks for Classification and Regression; Backpropagation in Practice
30. Radial Basis Function Networks; Gaussian RBF networks
31. Learning Weights in RBF networks; K-means clustering algorithm
32. Support Vector Machines -- Introduction, obtaining the optimal hyperplane
33. SVM formulation with slack variables; nonlinear SVM classifiers
34. Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels
35. Support Vector Regression and ?-insensitive Loss function, examples of SVM learning
36. Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer
37. Positive Definite Kernels; RKHS; Representer Theorem
38. Feature Selection and Dimensionality Reduction; Principal Component Analysis
39. No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off
40. Assessing Learnt classifiers; Cross Validation;
41. Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost
42. Risk minimization view of AdaBoost
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