大学学习 统计机器学习 全8讲

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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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