机器学习技法培训课程+机器学习基石第二部分视频教程 台大讲师林轩田倾力主讲

上半部分 https://www.kexuanwang.com/thread-25854-1-1.html

===============课程目录===============


├<机器学习基石_国立台湾大学(林轩田)>
│  ├10 – 1 – Logistic Regression Problem (14-33).mp4
│  ├10 – 2 – Logistic Regression Error (15-58).mp4
│  ├10 – 3 – Gradient of Logistic Regression Error (15-38).mp4
│  ├10 – 4 – Gradient Descent (19-18)(1).mp4
│  ├11 – 1 – Linear Models for Binary Classification (21-35).mp4
│  ├11 – 2 – Stochastic Gradient Descent (11-39).mp4
│  ├11 – 3 – Multiclass via Logistic Regression (14-18).mp4
│  ├11 – 4 – Multiclass via Binary Classification (11-35).mp4
│  ├12 – 1 – Quadratic Hypothesis (23-47).mp4
│  ├12 – 2 – Nonlinear Transform (09-52).mp4
│  ├12 – 3 – Price of Nonlinear Transform (15-37).mp4
│  ├12 – 4 – Structured Hypothesis Sets (09-36).mp4
│  ├13 – 1 – What is Overfitting- (10-45).mp4
│  ├13 – 2 – The Role of Noise and Data Size (13-36).mp4
│  ├13 – 3 – Deterministic Noise (14-07).mp4
│  ├13 – 4 – Dealing with Overfitting (10-49).mp4
│  ├14 – 1 – Regularized Hypothesis Set (19-16).mp4
│  ├14 – 2 – Weight Decay Regularization (24-08).mp4
│  ├14 – 3 – Regularization and VC Theory (08-15).mp4
│  ├14 – 4 – General Regularizers (13-28).mp4
│  ├15 – 1 – Model Selection Problem (16-00).mp4
│  ├15 – 2 – Validation (13-24).mp4
│  ├15 – 3 – Leave-One-Out Cross Validation (16-06).mp4
│  ├15 – 4 – V-Fold Cross Validation (10-41).mp4
│  ├16 – 1 – Occam-‘s Razor (10-08).mp4
│  ├16 – 2 – Sampling Bias (11-50).mp4
│  ├16 – 3 – Data Snooping (12-28).mp4
│  └16 – 4 – Power of Three (08-49).mp4
├<机器学习技法_国立台湾大学(林轩田)>
│  ├<01_Linear_Support_Vector_Machine>
│  │  ├01_Course_Introduction_4-07.mp4
│  │  ├01_Course_Introduction_4-07.pdf
│  │  ├02_Large-Margin_Separating_Hyperplane_14-17.mp4
│  │  ├03_Standard_Large-Margin_Problem_19-16.mp4
│  │  ├04_Support_Vector_Machine_15-33.mp4
│  │  └05_Reasons_behind_Large-Margin_Hyperplane_13-31.mp4
│  ├<02_Dual_Support_Vector_Machine>
│  │  ├01_Motivation_of_Dual_SVM_15-54.mp4
│  │  ├01_Motivation_of_Dual_SVM_15-54.pdf
│  │  ├02_Lagrange_Dual_SVM_18-50.mp4
│  │  ├03_Solving_Dual_SVM_14-19.mp4
│  │  └04_Messages_behind_Dual_SVM_11-18.mp4
│  ├<03_Kernel_Support_Vector_Machine>
│  │  ├01_Kernel_Trick_20-23.mp4
│  │  ├01_Kernel_Trick_20-23.pdf
│  │  ├02_Polynomial_Kernel_12-16.mp4
│  │  ├03_Gaussian_Kernel_14-43.mp4
│  │  └04_Comparison_of_Kernels_13-35.mp4
│  ├<04_Soft-Margin_Support_Vector_Machine>
│  │  ├01_Motivation_and_Primal_Problem_14-27.mp4
│  │  ├01_Motivation_and_Primal_Problem_14-27.pdf
│  │  ├02_Dual_Problem_7-38.mp4
│  │  ├03_Messages_behind_Soft-Margin_SVM_13-44.mp4
│  │  └04_Model_Selection_9-57.mp4
│  ├<05_Kernel_Logistic_Regression>
│  │  ├01_Soft-Margin_SVM_as_Regularized_Model_13-40.mp4
│  │  ├01_Soft-Margin_SVM_as_Regularized_Model_13-40.pdf
│  │  ├02_SVM_versus_Logistic_Regression_10-18.mp4
│  │  ├03_SVM_for_Soft_Binary_Classification_9-36.mp4
│  │  └04_Kernel_Logistic_Regression_16-22.mp4
│  ├<06_Support_Vector_Regression>
│  │  ├01_Kernel_Ridge_Regression_17-17.mp4
│  │  ├01_Kernel_Ridge_Regression_17-17.pdf
│  │  ├02_Support_Vector_Regression_Primal_18-44.mp4
│  │  ├03_Support_Vector_Regression_Dual_13-05.mp4
│  │  └04_Summary_of_Kernel_Models_09-06.mp4
│  ├<07_Blending_and_Bagging>
│  │  ├01_Motivation_of_Aggregation_18-54.mp4
│  │  ├01_Motivation_of_Aggregation_18-54.pdf
│  │  ├02_Uniform_Blending_20-31.mp4
│  │  ├03_Linear_and_Any_Blending_16-48.mp4
│  │  └04_Bagging_Bootstrap_Aggregation_11-48.mp4
│  ├<08_Adaptive_Boosting>
│  │  ├01_Motivation_of_Boosting_12-47.mp4
│  │  ├01_Motivation_of_Boosting_12-47.pdf
│  │  ├02_Diversity_by_Re-weighting_14-28.mp4
│  │  ├03_Adaptive_Boosting_Algorithm_13-34.mp4
│  │  └04_Adaptive_Boosting_in_Action_11-04.mp4
│  ├<09_Decision_Tree>
│  │  ├01_Decision_Tree_Hypothesis_17-28.mp4
│  │  ├01_Decision_Tree_Hypothesis_17-28.pdf
│  │  ├02_Decision_Tree_Algorithm_15-20.mp4
│  │  ├03_Decision_Tree_Heuristics_in_CRT_13-21.mp4
│  │  └04_Decision_Tree_in_Action_8-44.mp4
│  ├<10_Random_Forest>
│  │  ├01_Random_Forest_Algorithm_13-06.mp4
│  │  ├01_Random_Forest_Algorithm_13-06.pdf
│  │  ├02_Out-Of-Bag_Estimate_12-31.mp4
│  │  ├03_Feature_Selection_19-27.mp4
│  │  └04_Random_Forest_in_Action13-28.mp4
│  ├<11_Gradient_Boosted_Decision_Tree>
│  │  ├01_Adaptive_Boosted_Decision_Tree_15-05.mp4
│  │  ├01_Adaptive_Boosted_Decision_Tree_15-05.pdf
│  │  ├02_Optimization_View_of_AdaBoost_27-25.mp4
│  │  ├03_Gradient_Boosting_18-20.mp4
│  │  └04_Summary_of_Aggregation_Models_11-19.mp4
│  ├<12_Neural_Network>
│  │  ├01_Motivation_20-36.mp4
│  │  ├01_Motivation_20-36.pdf
│  │  ├02_Neural_Network_Hypothesis_18-01.mp4
│  │  ├03_Neural_Network_Learning_20-15.mp4
│  │  └04_Optimization_and_Regularization_17-29.mp4
│  ├<13_Deep_Learning>
│  │  ├01_Deep_Neural_Network_21-30.mp4
│  │  ├01_Deep_Neural_Network_21-30.pdf
│  │  ├02_Autoencoder_15-17.mp4
│  │  ├03_Denoising_Autoencoder_8-30.mp4
│  │  └04_Principal_Component_Analysis_31-20.mp4
│  ├<14_Radial_Basis_Function_Network>
│  │  ├01_RBF_Network_Hypothesis_12-55.mp4
│  │  ├01_RBF_Network_Hypothesis_12-55.pdf
│  │  ├02_RBF_Network_Learning_20-08.mp4
│  │  ├03_k-Means_Algorithm_16-19.mp4
│  │  └04_k-Means_and_RBF_Network_in_Action_9-46.mp4
│  ├<15_Matrix_Factorization>
│  │  ├15 – 1 – Linear Network Hypothesis (20-16).mp4
│  │  ├15 – 2 – Basic Matrix Factorization (16-32).mp4
│  │  ├15 – 3 – Stochastic Gradient Descent (12-22).mp4
│  │  ├15 – 4 – Summary of Extraction Models (9-12).mp4
│  │  └215_handout.pdf
│  ├<16_Finale>
│  │  ├16 – 1 – Feature Exploitation Techniques (16-11).mp4
│  │  ├16 – 2 – Error Optimization Techniques (8-40).mp4
│  │  ├16 – 3 – Overfitting Elimination Techniques (6-44).mp4
│  │  ├16 – 4 – Machine Learning in Action (12-59).mp4
│  │  └216_handout.pdf