Machine learning techniques

Linear support vector machine, dual support vector machine, kernel support vector machine, soft support vector machine, kernel logistic regression, support vector regression

• Chapter 1：Course Introduction (4 minutes and 7 seconds)

• Chapter 2：Large-Margin Separating Hyperplane (14 minutes and 17 seconds)

• Chapter 3：Standard Large-Margin Problem (19 minutes and 16 seconds)

• Chapter 4：Support Vector Machine (15 minutes and 33 seconds)

• Chapter 5：Reasons behind Large-Margin Hyperplane (13 minutes and 31 seconds)

• Chapter 6：Motivation of Dual SVM (15 minutes and 54 seconds)

• Chapter 7：Lagrange Dual SVM (18 minutes and 50 seconds)

• Chapter 8：Solving Dual SVM (14 minutes and 19 seconds)

• Chapter 9：Messages behind Dual SVM (11 minutes and 18 seconds)

• Chapter 10：Kernel Trick (20 minutes and 23 seconds)

• Chapter 11：Polynomial Kernel (12 minutes and 16 seconds)

• Chapter 12：Gaussian Kernel (14 minutes and 43 seconds)

• Chapter 13：Comparison of Kernels (13 minutes and 35 seconds)

• Chapter 14：Motivation and Primal Problem (14 minutes and 28 seconds)

• Chapter 15：Dual Problem (7 minutes and 38 seconds)

• Chapter 16：Messages behind Soft-Margin SVM (13 minutes and 44 seconds)

• Chapter 17：Model Selection (9 minutes and 57 seconds)

• Chapter 18：Soft-Margin SVM as Regularized Model (13 minutes and 40 seconds)

• Chapter 19：SVM versus Logistic Regression (10 minutes and 18 seconds)

• Chapter 20：SVM for Soft Binary Classification (9 minutes and 37 seconds)

• Chapter 21：Kernel Logistic Regression (16 minutes and 22 seconds)

• Chapter 22：Kernel Ridge Regression (17 minutes and 17 seconds)

• Chapter 23：Support Vector Regression Primal (18 minutes and 44 seconds)

• Chapter 24：Support Vector Regression Dual (13 minutes and 5 seconds)

• Chapter 25：Summary of Kernel Models (9 minutes and 6 seconds)

• Chapter 26：Motivation of Aggregation (18 minutes and 54 seconds)

• Chapter 27：Uniform Blending (20 minutes and 31 seconds)

• Chapter 28：Linear and Any Blending (16 minutes and 48 seconds)

• Chapter 29：Bagging (Bootstrap Aggregation) (11 minutes and 48 seconds)

• Chapter 30：Motivation of Boosting (12 minutes and 47 seconds)

• Chapter 31：Diversity by Re-weighting (14 minutes and 28 seconds)

• Chapter 32：Adaptive Boosting Algorithm (13 minutes and 34 seconds)

• Chapter 33：Adaptive Boosting in Action (11 minutes and 5 seconds)

• Chapter 34：Decision Tree Hypothesis (17 minutes and 28 seconds)

• Chapter 35：Decision Tree Algorithm (15 minutes and 20 seconds)

• Chapter 36：Decision Tree Heuristics in CRT (13 minutes and 21 seconds)

• Chapter 37：Decision Tree in Action (8 minutes and 44 seconds)

• Chapter 38：Random Forest Algorithm (13 minutes and 6 seconds)

• Chapter 39：Out-Of-Bag Estimate (12 minutes and 32 seconds)

• Chapter 40：Feature Selection (19 minutes and 27 seconds)

• Chapter 41：Random Forest in Action (13 minutes and 28 seconds)

• Chapter 42：Adaptive Boosted Decision Tree (15 minutes and 6 seconds)

• Chapter 43：Optimization View of AdaBoost (27 minutes and 25 seconds)

• Chapter 44：Gradient Boosting (18 minutes and 20 seconds)

• Chapter 45：Summary of Aggregation Models (11 minutes and 19 seconds)

• Chapter 46：Motivation (20 minutes and 36 seconds)

• Chapter 47：Neural Network Hypothesis (18 minutes and 1 seconds)

• Chapter 48：Neural Network Learning (20 minutes and 15 seconds)

• Chapter 49：Optimization and Regularization (19 minutes and 29 seconds)

• Chapter 50：Deep Neural Network (21 minutes and 30 seconds)

• Chapter 51：autoencoder (15 minutes and 17 seconds)

• Chapter 52：Denoising autoencoder (8 minutes and 31 seconds)

• Chapter 53：Principal Component Analysis (31 minutes and 20 seconds)

• Chapter 54：RBF Network Hypothesis (12 minutes and 55 seconds)

• Chapter 55：RBF Network Learning (20 minutes and 8 seconds)

• Chapter 56：k-Means Algorithm (16 minutes and 19 seconds)

• Chapter 57：k-Means and RBF Network in Action (9 minutes and 46 seconds)

• Chapter 58：Linear Network Hypothesis (20 minutes and 16 seconds)

• Chapter 59：Basic Matrix Factorization (16 minutes and 32 seconds)

• Chapter 60：Stochastic Gradient Descent (12 minutes and 22 seconds)

• Chapter 61：Summary of Extraction Models (9 minutes and 12 seconds)

• Chapter 62：Feature Exploitation Techniques (16 minutes and 11 seconds)

• Chapter 63：Error Optimization Techniques (8 minutes and 40 seconds)

• Chapter 64：Overfitting Elimination Techniques (6 minutes and 44 seconds)

• Chapter 65：Machine Learning in Action (12 minutes and 59 seconds)