Udemy, Coursera, 2U/edX Face Lawsuits Over Meta Pixel Use

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

NPTEL

Pattern Recognition

NPTEL and Indian Institute of Science Bangalore via YouTube

Start learning Write review

Overview

Prof. P.S. Sastry, Department of Electronics and Communication Engineering, IISc Bangalore.

This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier; parametric estimation of densities; mixture densities and EM algorithm; Nonparametric Density Estimation; Linear Models for Classification and Regression; overview of statistical learning theory; empirical risk minimization and VC-dimension; artificial neural networks for classification and regression; support vector machines and kernel-based methods; feature selection, model assessment and cross-validation; boosting and classifier ensembles.

Syllabus

Mod-08 Lec-28 Feedforward networks for Classification and Regression; Backpropagation in Practice.
Mod-06 Lec-13 Linear Discriminant Functions; Perceptron -- Learning Algorithm and convergence proof.
Mod-11 Lec-40 Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost.
Mod-05 Lec-12 Nonparametric estimation, Parzen Windows, nearest neighbour methods.
Mod-10 Lec-39 Assessing Learnt classifiers; Cross Validation;.
Mod-08 Lec-27 Backpropagation Algorithm; Representational abilities of feedforward networks.
Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;.
Mod-08 Lec-25 Overview of Artificial Neural Networks.
Mod-10 Lec-38 No Free Lunch Theorem; Model selection and model estimation; Bias-variance trade-off.
Mod-07 Lec-24 VC-Dimension Examples; VC-Dimension of hyperplanes.
Mod-04 & 05 Lec-11 Convergence of EM algorithm; overview of Nonparametric density estimation.
Mod-10 Lec-37 Feature Selection and Dimensionality Reduction; Principal Component Analysis.
Mod-07 Lec-23 Complexity of Learning problems and VC-Dimension.
Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm.
Mod-09 Lec-36 Positive Definite Kernels; RKHS; Representer Theorem.
Mod-03 Lec-09 Sufficient Statistics; Recursive formulation of ML and Bayesian estimates.
Mod-07 Lec-22 Consistency of Empirical Risk Minimization; VC-Dimension.
Mod-09 Lec-35 Overview of SMO and other algorithms for SVM; ?-SVM and ?-SVR; SVM as a risk minimizer.
Mod-03 Lec-08 Bayesian Estimation examples; the exponential family of densities and ML estimates.
Mod-09 Lec-34 Support Vector Regression and ?-insensitive Loss function, examples of SVM learning.
Mod-03 Lec-07 Bayesian estimation of parameters of density functions, MAP estimates.
Mod-07 Lec-21 Consistency of Empirical Risk Minimization.
Mod-07 Lec-20 Overview of Statistical Learning Theory; Empirical Risk Minimization.
Mod-07 Lec-19 Learning and Generalization; PAC learning framework.
Mod-03 Lec-06 Maximum Likelihood estimation of different densities.
Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels.
Mod-03 Lec-05 Implementing Bayes Classifier; Estimation of Class Conditional Densities.
Mod-06 Lec-18 Linear Discriminant functions for multi-class case; multi-class logistic regression.
Mod-02 Lec-04 Estimating Bayes Error; Minimax and Neymann-Pearson classifiers.
Mod-09 Lec-32 SVM formulation with slack variables; nonlinear SVM classifiers.

Taught by

nptelhrd

Tags

Reviews

Start your review of Pattern Recognition

Start learning

Never Stop Learning.

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

Sign up for free
Someone learning on their laptop while sitting on the floor.