Electronics - Pattern Recognition (English)

   Saturday, April 12, 2025   

Starting a new Lecture Notes Series on Electronics - Pattern Recognition

Youtube Lecture Playlist Credits 

Channel Name: nptelhrd

So Let Us Start to This Journey of Learning 

Electronics - Pattern Recognition By Lecture Notes together!

Lecture 1: Mod-01 Lec-01 Introduction to Statistical Pattern Recognition

Lecture 2: Mod-01 Lec-02 Overview of Pattern Classifiers

Lecture 3: Mod-02 Lec-03 The Bayes Classifier for minimizing Risk

Lecture 4: Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers

Lecture 5: Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities

Lecture 6: Mod-03 Lec-06 Maximum Likelihood estimation of different densities

Lecture 7: Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates

Lecture 8: Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML estimates

Lecture 9: Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates

Lecture 10: Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm

Lecture 11: Mod-04 & 05 Lec-11 Convergence of EM algorithm; overview of Nonparametric density estimation

Lecture 12: Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods

Lecture 13: Mod-06 Lec-13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof

Lecture 14: Mod-06 Lec-14 Linear Least Squares Regression; LMS algorithm

Lecture 15: Mod-06 Lec-15 AdaLinE and LMS algorithm; General nonliner least-squares regression

Lecture 16: Mod-06 Lec-16 Logistic Regression; Statistics of least squares method; Regularized Least Squares

Lecture 17: Mod-06 Lec-17 Fisher Linear Discriminant

Lecture 18: Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic regression

Lecture 19: Mod-07 Lec-19 Learning and Generalization; PAC learning framework

Lecture 20: Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization

Lecture 21: Mod-07 Lec-21 Consistency of Empirical Risk Minimization

Lecture 22: Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension

Lecture 23: Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension

Lecture 24: Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes

Lecture 25: Mod-08 Lec-25 Overview of Artificial Neural Networks

Lecture 26: Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;

Lecture 27: Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward networks

Lecture 28: Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in Practice

Lecture 29: Mod-08 Lec-29 Radial Basis Function Networks; Gaussian RBF networks

Lecture 30: Mod-08 Lec-30 Learning Weights in RBF networks; K-means clustering algorithm

Lecture 31: Mod-09 Lec-31 Support Vector Machines -- Introduction, obtaining the optimal hyperplane

Lecture 32: Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers

Lecture 33: Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels

Lecture 34: Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning

Lecture 35: Mod-09 Lec-35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer

Lecture 36: Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem

Lecture 37: Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis

Lecture 38: Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off

Lecture 39: Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation;

Lecture 40: Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost

Lecture 41: Mod-11 Lec-41 Risk minimization view of AdaBoost