课时1：Course Introduction

课时2：Large-Margin Separating Hyperplane

课时3：Standard Large-Margin Problem

课时4：Support Vector Machine

课时5：Reasons behind Large-Margin Hyperplane

课时6：Motivation of Dual SVM

课时7：Lagrange Dual SVM

课时8：Solving Dual SVM

课时9：Messages behind Dual SVM

课时10：Kernel Trick

课时11：Polynomial Kernel

课时12：Gaussian Kernel

课时13：Comparison of Kernels

课时14：Motivation and Primal Problem

课时15：Dual Problem

课时16：Messages behind Soft-Margin SVM

课时17：Model Selection

课时18：Soft-Margin SVM as Regularized Model

课时19：SVM versus Logistic Regression

课时20：SVM for Soft Binary Classification

课时21：Kernel Logistic Regression

课时22：Kernel Ridge Regression

课时23：Support Vector Regression Primal

课时24：Support Vector Regression Dual

课时25：Summary of Kernel Models

课时26：Motivation of Aggregation

课时27：Uniform Blending

课时28：Linear and Any Blending

课时29：Bagging (Bootstrap Aggregation)

课时30：Motivation of Boosting

课时31：Diversity by Re-weighting

课时32：Adaptive Boosting Algorithm

课时33：Adaptive Boosting in Action

课时34：Decision Tree Hypothesis

课时35：Decision Tree Algorithm

课时36：Decision Tree Heuristics in CRT

课时37：Decision Tree in Action

课时38：Random Forest Algorithm

课时39：Out-Of-Bag Estimate

课时40：Feature Selection

课时41：Random Forest in Action

课时42：Adaptive Boosted Decision Tree

课时43：Optimization View of AdaBoost

课时44：Gradient Boosting

课时45：Summary of Aggregation Models

课时46：Motivation

课时47：Neural Network Hypothesis

课时48：Neural Network Learning

课时49：Optimization and Regularization

课时50：Deep Neural Network

课时51：Autoencoder

课时52：Denoising Autoencoder

课时53：Principal Component Analysis

课时54：RBF Network Hypothesis

课时55：RBF Network Learning

课时56：k-Means Algorithm

课时57：k-Means and RBF Network in Action

课时58：Linear Network Hypothesis

课时59：Basic Matrix Factorization

课时60：Stochastic Gradient Descent

课时61：Summary of Extraction Models

课时62：Feature Exploitation Techniques

课时63：Error Optimization Techniques

课时64：Overfitting Elimination Techniques

课时65：Machine Learning in Action