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Computer Engineering

Pattern Recognition (Prof. P.S. Sastry, IISc Bangalore)

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September 26, 2023

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Description

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

Overview of Pattern Classification and Regression
- Lecture 01 – Instruction to Statistical Pattern Recognition Unlimited
- Lecture 02 – Overview of Pattern Classifiers Unlimited
Bayesian Decision Making and Bayes Classifier
- Lecture 03 – The Bayes Classifier for Minimizing Risk Unlimited
- Lecture 04 – Estimating Bayes Error; Minimax and Neyman-Pearson Classifiers Unlimited
Parametric Estimation of Densities
- 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
Mixture Densities and EM Algorithm, Nonparametric Density Estimation
- 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
Linear Models for Classification and Regression
- Lecture 13 – Linear Discriminant Function; Perceptron – Learning Algorithm and Convergence Proof Unlimited
- https://www.youtube.com/watch?v=akeUZ2aVQxw 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
Overview of Statistical Learning Theory, Empirical Risk Minimization and VC-Dimension
- 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
Artificial Neural Networks for Classification and Regression
- 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
Support Vector Machines and Kernel Based Methods
- 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
Feature Selection, Model Assessment and Cross-validation
- 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
Boosting and Classifier Ensembles
- Lecture 40 – Bootstrap, Bagging and Boosting; Classifier Ensembles; AdaBoost Unlimited
- Lecture 41 – Risk Minimization View of AdaBoost Unlimited

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