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| 01_Bayesian_Methods.mp4 |
20.48Мб |
| 01_Class_Information.mp4 |
25.27Мб |
| 01_Clustering_and_Dimensionality_Reduction.mp4 |
34.03Мб |
| 01_Decision_Trees.mp4 |
41.30Мб |
| 01_Learning_Theory.mp4 |
12.80Мб |
| 01_Model_Ensembles.mp4 |
13.35Мб |
| 01_Reverse-Engineering_the_Brain.mp4 |
52.70Мб |
| 01_Rules_vs._Decision_Trees.mp4 |
67.21Мб |
| 01_Support_Vector_Machines.mp4 |
30.82Мб |
| 01_The_K-Nearest_Neighbor_Algorithm.mp4 |
69.21Мб |
| 02_Bagging.mp4 |
38.00Мб |
| 02_Bayes_Theorem_and_MAP_Hypotheses.mp4 |
102.33Мб |
| 02_K-Means_Clustering.mp4 |
44.37Мб |
| 02_Learning_a_Set_of_Rules.mp4 |
50.41Мб |
| 02_Neural_Network_Driving_a_Car.mp4 |
46.67Мб |
| 02_No_Free_Lunch_Theorems.mp4 |
59.89Мб |
| 02_Perceptrons_as_Instance-Based_Learning.mp4 |
51.77Мб |
| 02_Theoretical_Guarantees_on_k-NN.mp4 |
43.24Мб |
| 02_What_Can_a_Decision_Tree_Represent.mp4 |
27.24Мб |
| 02_What_Is_Machine_Learning.mp4 |
38.85Мб |
| 03_Applications_of_Machine_Learning.mp4 |
39.90Мб |
| 03_Basic_Probability_Formulas.mp4 |
24.03Мб |
| 03_Boosting-_The_Basics.mp4 |
34.22Мб |
| 03_Distance-Weighted_k-NN.mp4 |
12.05Мб |
| 03_Estimating_Probabilities_from_Small_Samples.mp4 |
36.45Мб |
| 03_Growing_a_Decision_Tree.mp4 |
27.13Мб |
| 03_How_Neurons_Work.mp4 |
34.52Мб |
| 03_Kernels.mp4 |
67.50Мб |
| 03_Mixture_Models.mp4 |
53.02Мб |
| 03_Practical_Consequences_of_No_Free_Lunch.mp4 |
34.98Мб |
| 04_Accuracy_and_Information_Gain.mp4 |
86.19Мб |
| 04_Bias_and_Variance.mp4 |
77.17Мб |
| 04_Boosting-_The_Details.mp4 |
49.38Мб |
| 04_Key_Elements_of_Machine_Learning.mp4 |
76.56Мб |
| 04_Learning_Rules_for_Multiple_Classes.mp4 |
22.69Мб |
| 04_Learning_SVMs.mp4 |
64.76Мб |
| 04_MAP_Learning.mp4 |
57.72Мб |
| 04_Mixtures_of_Gaussians.mp4 |
20.75Мб |
| 04_The_Curse_of_Dimensionality.mp4 |
58.65Мб |
| 04_The_Perceptron.mp4 |
50.93Мб |
| 05_Bias-Variance_Decomposition_for_Squared_Loss.mp4 |
15.85Мб |
| 05_Constrained_Optimization.mp4 |
75.23Мб |
| 05_EM_Algorithm_for_Mixtures_of_Gaussians.mp4 |
43.26Мб |
| 05_Error-Correcting_Output_Coding.mp4 |
39.36Мб |
| 05_Feature_Selection_and_Weighting.mp4 |
47.79Мб |
| 05_First-Order_Rules.mp4 |
45.12Мб |
| 05_Learning_a_Real-Valued_Function.mp4 |
43.55Мб |
| 05_Learning_with_Non-Boolean_Features.mp4 |
25.36Мб |
| 05_Perceptron_Training.mp4 |
48.61Мб |
| 05_Types_of_Learning.mp4 |
61.34Мб |
| 06_Bayes_Optimal_Classifier_and_Gibbs_Classifier.mp4 |
40.40Мб |
| 06_General_Bias-Variance_Decomposition.mp4 |
43.90Мб |
| 06_Gradient_Descent.mp4 |
36.77Мб |
| 06_Learning_First-Order_Rules_Using_FOIL.mp4 |
97.29Мб |
| 06_Machine_Learning_in_Practice.mp4 |
46.47Мб |
| 06_Mixture_Models_vs._K-Means_vs._Bayesian_Networks.mp4 |
27.96Мб |
| 06_Optimization_with_Inequality_Constraints.mp4 |
52.86Мб |
| 06_Reducing_the_Computational_Cost_of_k-NN.mp4 |
44.76Мб |
| 06_Stacking.mp4 |
42.27Мб |
| 06_The_Parity_Problem.mp4 |
19.14Мб |
| 07_Avoiding_Overfitting_in_k-NN.mp4 |
26.17Мб |
| 07_Bias-Variance_Decomposition_for_Zero-One_Loss.mp4 |
25.60Мб |
| 07_Gradient_Descent_Continued.mp4 |
37.40Мб |
| 07_Hierarchical_Clustering.mp4 |
19.67Мб |
| 07_Induction_as_Inverted_Deduction.mp4 |
74.55Мб |
| 07_Learning_with_Many-Valued_Attributes.mp4 |
22.52Мб |
| 07_The_Naive_Bayes_Classifier.mp4 |
102.43Мб |
| 07_The_SMO_Algorithm.mp4 |
24.18Мб |
| 07_What_Is_Inductive_Learning.mp4 |
14.93Мб |
| 08_Bias_and_Variance_for_Other_Loss_Functions.mp4 |
15.83Мб |
| 08_Gradient_Descent_vs._Perceptron_Training.mp4 |
24.72Мб |
| 08_Handling_Noisy_Data_in_SVMs.mp4 |
55.10Мб |
| 08_Inverting_Propositional_Resolution.mp4 |
63.90Мб |
| 08_Learning_with_Missing_Values.mp4 |
37.86Мб |
| 08_Locally_Weighted_Regression.mp4 |
20.03Мб |
| 08_Principal_Components_Analysis.mp4 |
58.25Мб |
| 08_Text_Classification.mp4 |
42.98Мб |
| 08_When_Should_You_Use_Inductive_Learning.mp4 |
27.91Мб |
| 09_Bayesian_Networks.mp4 |
93.07Мб |
| 09_Generalization_Bounds_for_SVMs.mp4 |
41.28Мб |
| 09_Inverting_First-Order_Resolution.mp4 |
86.69Мб |
| 09_Multidimensional_Scaling.mp4 |
28.35Мб |
| 09_PAC_Learning.mp4 |
40.03Мб |
| 09_Radial_Basis_Function_Networks.mp4 |
13.34Мб |
| 09_Stochastic_Gradient_Descent.mp4 |
18.20Мб |
| 09_The_Essence_of_Inductive_Learning.mp4 |
99.08Мб |
| 09_The_Overfitting_Problem.mp4 |
48.33Мб |
| 10_A_Framework_for_Studying_Inductive_Learning.mp4 |
94.53Мб |
| 10_Case-Based_Reasoning.mp4 |
16.04Мб |
| 10_Decision_Tree_Pruning.mp4 |
79.50Мб |
| 10_How_Many_Examples_Are_Enough.mp4 |
54.99Мб |
| 10_Inference_in_Bayesian_Networks.mp4 |
15.43Мб |
| 10_Multilayer_Perceptrons.mp4 |
61.83Мб |
| 10_Nonlinear_Dimensionality_Reduction.mp4 |
45.61Мб |
| 11_Backpropagation.mp4 |
81.95Мб |
| 11_Bayesian_Network_Review.mp4 |
16.45Мб |
| 11_Examples_and_Definition_of_PAC_Learning.mp4 |
17.34Мб |
| 11_Lazy_vs._Eager_Learning.mp4 |
11.32Мб |
| 11_Post-Pruning_Trees_to_Rules.mp4 |
94.41Мб |
| 12_Agnostic_Learning.mp4 |
45.76Мб |
| 12_Collaborative_Filtering.mp4 |
70.53Мб |
| 12_Issues_in_Backpropagation.mp4 |
100.65Мб |
| 12_Learning_Bayesian_Networks.mp4 |
15.38Мб |
| 12_Scaling_Up_Decision_Tree_Learning.mp4 |
27.95Мб |
| 13_Learning_Hidden_Layer_Representations.mp4 |
57.15Мб |
| 13_The_EM_Algorithm.mp4 |
53.92Мб |
| 13_VC_Dimension.mp4 |
39.97Мб |
| 14_Example_of_EM.mp4 |
55.26Мб |
| 14_Expressiveness_of_Neural_Networks.mp4 |
29.44Мб |
| 14_VC_Dimension_of_Hyperplanes.mp4 |
39.28Мб |
| 15_Avoiding_Overfitting_in_Neural_Networks.mp4 |
37.83Мб |
| 15_Learning_Bayesian_Network_Structure.mp4 |
71.49Мб |
| 15_Sample_Complexity_from_VC_Dimension.mp4 |
7.72Мб |
| 16_The_Structural_EM_Algorithm.mp4 |
286.36Мб |
| entered_login.html |
1.31Мб |