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【大学学习】统计机器学习 全8讲

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  • 类别:博学考试 > 大学学习 Tags:大学学习  
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 【大学学习】统计机器学习 全8讲

索引: Outline(00:00:08) 
Challenging problems(00:00:19) 
Data Mining(00:00:53) 
Machine Learning(00:02:15) 
Application in PR(00:03:14) 
Difference(00:03:28) 
Biometrics(00:04:04) 
Bioinformatics(00:04:39) 
ISI(00:05:08) 
Confusion(00:05:34) 
统计机器学习基础研究(00:06:00) 
Machine learning community(00:06:31) 
学习(00:06:55) 
Performance(00:08:15) 
学习(00:08:19) 
Performance(00:08:22) 
More(00:08:53) 
Theoretical Analysis(00:09:11) 
Ian Hacking(00:09:44) 
Statistical learning(00:10:28) 
Andreas Buja(00:10:46) 
Interpretation of Algorithms(00:11:22) 
统计学习(00:11:58) 
Main references(00:13:18) 
Main kinds of theory(00:13:39) 
Definition of Classifications(00:14:02) 
统计学习(00:14:23) 
Main kinds of theory(00:15:21) 
Definition of Classifications(00:15:22) 
Definition of regression(00:15:50) 
Several well-known algorithms(00:16:27) 
Framework of algorithms(00:17:02) 
Designation of algorithms(00:17:58) 
统计决策理论(00:18:39) 
Bayesian:classification(00:19:26) 
统计决策理论(00:20:10) 
Bayesian:classification(00:20:13) 
Bayesian: regression(00:20:18) 
统计决策理论(00:20:55) 
Bayesian:classification(00:21:00) 
Bayesian: regression(00:21:17) 
Estimating densities(00:21:25) 
KNN(00:22:45) 
Interpretation:KNN(00:23:20) 
高维空间(00:24:15) 
维数灾难(00:25:01) 
维数灾难(00:25:50) 
维数灾难:其它体现(00:26:45) 
LMS(00:27:33) 
Interpretation: LMS(00:29:57) 
维数灾难(00:30:57) 
KNN(00:30:58) 
Designation of algorithms(00:30:59) 
Designation of algorithms(00:31:00) 
统计决策理论(00:31:01) 
Estimating densities(00:31:18) 
高维空间(00:31:19) 
维数灾难:其它体现(00:31:20) 
Interpretation: LMS(00:31:21) 
Fisher Discriminant Analysis(00:31:40) 
Interpretation: FDA(00:32:35) 
FDA and LMS(00:33:04) 
FDA: a novel interpretation(00:33:38) 
FDA: parameters(00:34:24) 
FDA: framework of algorithms(00:35:09) 
Disadvantage(00:35:59) 
Bias and variance analysis(00:36:44) 
Bias-Variance Decomposition(00:37:17) 
Bias-Variance Tradeoff(00:38:46) 
Bias-Variance Decomposition(00:38:52) 
Bias-Variance Tradeoff(00:39:05) 
Interpretation: KNN(00:40:29) 
Ridge regression(00:41:35) 
Interpretation: ridge regression(00:42:03) 
Ridge regression(00:42:43) 
Interpretation: ridge regression(00:43:05) 
Interpretation: parameter(00:43:28) 
Interpretation: ridge regression(00:43:35) 
Interpretation: parameter(00:43:37) 
A note(00:44:32) 
Other loss functions(00:45:39) 
Interpretation: boosting(00:46:35) 
Boosting方法的由来(00:47:22) 
Boosting方法流程(AdaBoost)(00:48:18) 
Interpretation: margin(00:48:47) 
Interpretation: SVM(00:49:43) 
SVM: experimental analysis(00:50:48) 
Interpretation: base learners(00:51:57) 
Disadvantage(00:52:38) 
Generalization bound(00:53:15) 
PAC Frame(00:54:16) 
VC Theory and PAC Bounds(00:54:44) 
PAC Bounds for Classification(00:55:38) 
VC Dimension(00:55:51) 
PAC Bounds for Classification(00:55:52) 
VC Dimension(00:56:27) 
A consistency problems(00:57:39) 
Remarks on PAC+VC Bounds(00:58:33) 
SVM: Linearly separable(00:59:21) 
SVM: soft Margin(01:00:28) 
SVM: Linearly separable(01:01:12) 
SVM: soft Margin(01:01:22) 
SVM: algorithms(01:01:59) 
泛化能力的界(01:03:01) 
Bound: VC Dimension(01:04:04) 
Bound: VC dimension+errors(01:04:45) 
Disadvantages of SRM(01:05:52) 
Disadvantage: PAC+VC bound(01:06:52) 
Several concepts(01:07:51) 
Disadvantage: PAC+VC bound(01:08:00) 
Several concepts(01:08:02) 
Generalization Bound: margin(01:08:35) 
Importance of Margin(01:09:48) 
Generalization Bound: margin(01:10:29) 
Importance of Margin(01:10:34) 
Vapnik’s three periods(01:10:35) 
Neural networks(01:11:51) 
Interpretation: neural networks(01:12:55) 
BP Algorithms(01:14:17) 
Disadvantage(01:15:42) 
The End(01:16:32)
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