Pattern Recognition

Pattern Recognition

nptelhrd via YouTube Direct link

Mod-01 Lec-01 Introduction to Statistical Pattern Recognition

1 of 41

1 of 41

Mod-01 Lec-01 Introduction to Statistical Pattern Recognition

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Pattern Recognition

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Mod-01 Lec-01 Introduction to Statistical Pattern Recognition
  2. 2 Mod-01 Lec-02 Overview of Pattern Classifiers
  3. 3 Mod-02 Lec-03 The Bayes Classifier for minimizing Risk
  4. 4 Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers
  5. 5 Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities
  6. 6 Mod-03 Lec-06 Maximum Likelihood estimation of different densities
  7. 7 Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates
  8. 8 Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML estimates
  9. 9 Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates
  10. 10 Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm
  11. 11 Mod-04 & 05 Lec-11 Convergence of EM algorithm; overview of Nonparametric density estimation
  12. 12 Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods
  13. 13 Mod-06 Lec-13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof
  14. 14 Mod-06 Lec-14 Linear Least Squares Regression; LMS algorithm
  15. 15 Mod-06 Lec-15 AdaLinE and LMS algorithm; General nonliner least-squares regression
  16. 16 Mod-06 Lec-16 Logistic Regression; Statistics of least squares method; Regularized Least Squares
  17. 17 Mod-06 Lec-17 Fisher Linear Discriminant
  18. 18 Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic regression
  19. 19 Mod-07 Lec-19 Learning and Generalization; PAC learning framework
  20. 20 Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization
  21. 21 Mod-07 Lec-21 Consistency of Empirical Risk Minimization
  22. 22 Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension
  23. 23 Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension
  24. 24 Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes
  25. 25 Mod-08 Lec-25 Overview of Artificial Neural Networks
  26. 26 Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;
  27. 27 Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward networks
  28. 28 Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in Practice
  29. 29 Mod-08 Lec-29 Radial Basis Function Networks; Gaussian RBF networks
  30. 30 Mod-08 Lec-30 Learning Weights in RBF networks; K-means clustering algorithm
  31. 31 Mod-09 Lec-31 Support Vector Machines -- Introduction, obtaining the optimal hyperplane
  32. 32 Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers
  33. 33 Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels
  34. 34 Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning
  35. 35 Mod-09 Lec-35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer
  36. 36 Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem
  37. 37 Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis
  38. 38 Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off
  39. 39 Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation;
  40. 40 Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost
  41. 41 Mod-11 Lec-41 Risk minimization view of AdaBoost

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.