Created by:

Profile Photo

Last updated:

September 25, 2023


Unlimited Duration


This course includes:

Unlimited Duration

Badge on Completion

Certificate of completion

Unlimited Duration


Pattern Recognition. Instructor: Prof. P.S. Sastry, Department of Electronics and Communication Engineering, IISc Bangalore.

This course provides a fairly comprehensive view of fundamentals of pattern classification and regression. Topics covered in the lectures include: 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. (from nptel.ac.in)

Course Curriculum

    • Lecture 01 – Instruction to Statistical Pattern Recognition Unlimited
    • Lecture 02 – Overview of Pattern Classifiers Unlimited
    • Lecture 03 – The Bayes Classifier for Minimizing Risk Unlimited
    • Lecture 04 – Estimating Bayes Error; Minimax and Neyman-Pearson Classifiers Unlimited
    • Lecture 05 – Implementing Bayes Classifier; Estimation of Class Conditional Densities Unlimited
    • Lecture 06 – Maximum Likelihood Estimation of Different Densities Unlimited
    • Lecture 07 – Bayesian Estimation of Parameters of Density Functions, MAP Estimates Unlimited
    • Lecture 08 – Bayesian Estimation Examples; the Exponential Family of Densities and ML Estimates Unlimited
    • Lecture 09 – Sufficient Statistics; Recursive Formulation of ML and Bayesian Estimates Unlimited
    • Lecture 10 – Mixture Densities, ML Estimation and EM Algorithm Unlimited
    • Lecture 11 – Convergence of EM Algorithm; Overview of Nonparametric Density Estimation Unlimited
    • Lecture 12 – Nonparametric Estimation, Parzen Windows, Nearest Neighbor Methods Unlimited
    • Lecture 13 – Linear Discriminant Function; Perceptron – Learning Algorithm and Convergence Proof Unlimited
    • Lecture 14 – Linear Least Squares Regression; LMS Algorithm Unlimited
    • Lecture 15 – AdaLinE and LMS Algorithm; General Nonlinear Least Squares Regression Unlimited
    • Lecture 16 – Logistic Regression; Statistics of Least Squares Method; Regulated Least Squares Unlimited
    • Lecture 17 – Fisher Linear Discriminant Unlimited
    • Lecture 18 – Linear Discriminant Functions for Multi-Class Case; Multi-Class Logistic Regression Unlimited
    • Lecture 19 – Learning and Generalization; PAC Learning Framework Unlimited
    • Lecture 20 – Overview of Statistical Learning Theory; Empirical Risk Minimization Unlimited
    • Lecture 21 – Consistency of Empirical Risk Minimization Unlimited
    • Lecture 22 – Consistency of Empirical Risk Minimization; VC-Dimension Unlimited
    • Lecture 23 – Complexity of Learning Problems and VC-Dimension Unlimited
    • Lecture 24 – VC-Dimension Examples; VC-Dimension of Hyperplanes Unlimited
    • Lecture 25 – Overview of Artificial Neural Networks Unlimited
    • Lecture 26 – Multilayer Feedforward Neural Networks with Sigmoidal Activation Functions Unlimited
    • Lecture 27 – Backpropagation Algorithm; Representational Abilities of Feedforward Networks Unlimited
    • Lecture 28 – Feedforward Networks for Classification and Regression; Backpropagation in Practice Unlimited
    • Lecture 29 – Radial Basis Function Networks; Gaussian RBF Networks Unlimited
    • Lecture 30 – Learning Weights in RBF Networks; K-means Clustering Algorithm Unlimited
    • Lecture 31 – Support Vector Machines – Introduction, Obtaining the Optimal Hyperplane Unlimited
    • Lecture 32 – SVM Formulation with Slack Variables; Nonlinear SVM Classifiers Unlimited
    • Lecture 33 – Kernel Functions for Nonlinear SVMs; Mercer and Positive Definite Kernels Unlimited
    • Lecture 34 – Support Vector Regression and E-intensive Loss Function, Examples of SVM Learning Unlimited
    • Lecture 35 – Overview of SMO and Other Algorithms for SVM; v-SVM and v-SVR Unlimited
    • Lecture 36 – Positive Definite Kernels; RKHS; Representer Theorem Unlimited
    • Lecture 37 – Feature Selection and Dimensionality Reduction; Principal Component Analysis Unlimited
    • Lecture 38 – No Free Lunch Theorem; Model Selection and Model Estimation; Bias-variance Trade-off Unlimited
    • Lecture 39 – Assessing Learnt Classifiers; Cross Validation Unlimited
    • Lecture 40 – Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost Unlimited
    • Lecture 41 – Risk Minimization View of AdaBoost Unlimited

About the instructor

5 5

Instructor Rating







Profile Photo
We are an educational and skills marketplace to accommodate the needs of skills enhancement and free equal education across the globe to the millions. We are bringing courses and trainings every single day for our users. We welcome everyone woth all ages, all background to learn. There is so much available to learn and deliver to the people.