大学学习 统计机器学习 全8讲——更多资源,课程更新在

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)