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