**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 |