Electronics - Pattern Recognition (English)

   Saturday, April 12, 2025   

Starting a new Lecture Notes Series on Electronics - Pattern Recognition

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Electronics - Pattern Recognition By Lecture Notes together!

Lecture 1: Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in Practice

Lecture 2: Mod-06 Lec-13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof

Lecture 3: Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost

Lecture 4: Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods

Lecture 5: Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation;

Lecture 6: Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward networks

Lecture 7: Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;

Lecture 8: Mod-08 Lec-25 Overview of Artificial Neural Networks

Lecture 9: Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off

Lecture 10: Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes

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

Lecture 12: Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis

Lecture 13: Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension

Lecture 14: Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm

Lecture 15: Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem

Lecture 16: Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates

Lecture 17: Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension

Lecture 18: Mod-09 Lec-35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer

Lecture 19: Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML estimates

Lecture 20: Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning

Lecture 21: Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates

Lecture 22: Mod-07 Lec-21 Consistency of Empirical Risk Minimization

Lecture 23: Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization

Lecture 24: Mod-07 Lec-19 Learning and Generalization; PAC learning framework

Lecture 25: Mod-03 Lec-06 Maximum Likelihood estimation of different densities

Lecture 26: Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels

Lecture 27: Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities

Lecture 28: Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic regression

Lecture 29: Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers

Lecture 30: Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers