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机器学习技法（林轩田）

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https://www.youtube.com/playlist?list=PLXVfgk9fNX2IQOYPmqjqWsNUFl2kpk1U2 Machine Learning Techniques (機器學習技法)
AI
机器学习
深度学习
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视频选集
（1/65）
1.1 Linear SVM - Course Introduction
04:08
1.2 Linear SVM - Large-Margin Separating Hyperplane
14:18
1.3 Linear SVM - Standard Large-Margin Problem
19:18
1.4 Linear SVM - Support Vector Machine
15:34
1.5 Linear SVM - Reasons behind Large-Margin Hyperplane
13:32
2.1 Dual Support Vector Machine - Motivation of Dual SVM
15:55
2.2 Dual Support Vector Machine - Largange Dual SVM
18:51
2.3 Dual Support Vector Machine - Solving Dual SVM
14:20
2.4 Dual Support Vector Machine - Messages behind Dual SVM
11:19
3.1 Kernel Support Vector Machine - Kernel Trick
20:24
3.2 Kernel Support Vector Machine - Polynomial Kernel
12:17
3.3 Kernel Support Vector Machine - Gaussian Kernel
14:44
3.4 Kernel Support Vector Machine - Comparison of Kernels
13:36
4.1 Soft-Margin Support Vector Machine - Motivation and Primal
14:29
4.2 Soft-Margin Support Vector Machine - Dual Problem
07:39
4.3 Soft-Margin Support Vector Machine - Messages
13:45
4.4 Soft-Margin Support Vector Machine - Model Selection
09:58
5.1 Kernel Logistic Regression - Soft-Margin SVM as Regularized
13:42
5.2 Kernel Logistic Regression - SVM versus Logistic Regression
10:19
5.3 Kernel Logistic Regression - SVM for Soft Binary
09:37
5.4 Kernel Logistic Regression - Kernel Logistic Regression
16:23
6.1 Support Vector Regression - Kernel Ridge Regression
17:18
6.2 Support Vector Regression - Support Vector Regression Primal
18:45
6.3 Support Vector Regression - Support Vector Regression Dual
13:06
6.4 Support Vector Regression - Summary of Kernel Models
09:07
7.1 Blending and Bagging - Motivation of Aggregation
18:56
7.2 Blending and Bagging - Uniform Blending
20:32
7.3 Blending and Bagging - Linear and Any Blending
16:49
7.4 Blending and Bagging - Bagging (Bootstrap Aggregation)
11:49
8.1 Adaptive Boosting - Motivation of Boosting
12:48
8.2 Adaptive Boosting - Diversity by Re-weighting
14:29
8.3 Adaptive Boosting - Adaptive Boosting Algorithm
13:35
8.4 Adaptive Boosting - Adaptive Boosting in Action
11:05
9.1 Decision Tree - Decision Tree Hypothesis
17:30
9.2 Decision Tree - Decision Tree Algorithm
15:21
9.3 Decision Tree - Decision Tree Heuristics in C&RT
13:22
9.4 Decision Tree - Decision Tree in Action
08:45
10.1 Random Forest - Random Forest Algorithm
13:07
10.2 Random Forest - Out-of-bag Estimate
12:33
10.3 Random Forest - Feature Selection
19:28
10.4 Random Forest - Random Forest in Action
13:29
11.1 Gradient Boosted Decision Tree - AdaBoost Decision Tree
15:07
11.2 Gradient Boosted Decision Tree - Optimization of AdaBoost
27:27
11.3 Gradient Boosted Decision Tree - Gradient Boosting
18:21
11.4 Gradient Boosted Decision Tree - Summary of Aggregation
11:20
12.1 Neural Network - Motivation
20:38
12.2 Neural Network - Neural Network Hypothesis
18:03
12.3 Neural Network - Neural Network Learning
22:27
12.4 Neural Network - Optimization and Regularization
17:30
13.1 Deep Learning - Deep Neural Network
21:31
13.2 Deep Learning -Autoencoder
15:18
13.3 Deep Learning -Denoising Autoencoder
08:31
13.4 Deep Learning - Principal Component Analysis
31:22
14.1 Radial Basis Function Network - RBF Network Hypothesis
12:56
14.2 Radial Basis Function Network - RBF Network Learning
20:09
14.3 Radial Basis Function Network - k-Means Algorithm
16:20
14.4 Radial Basis Function Network - k-Means and RBFNet in Action
09:47
15.1 Matrix Factorization - Linear Network Hypothesis
20:17
15.2 Matrix Factorization - Basic Matrix Factorization
16:33
15.3 Matrix Factorization - Stochastic Gradient Descent
12:23
15.4 Matrix Factorization - Summary of Extraction Models
09:13
16.1 Finale - Feature Exploitation Techniques
16:12
16.2 Finale - Error Optimization Techniques
08:40
16.3 Finale - Overfitting Elimination Techniques
06:44
16.4 Finale - Machine Learning in Practice
13:00

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