Machlearning 001 | 0CDBA976D648FBE322133833323491EBF8B34340

Machlearning 001

Name	machlearning-001	DOWNLOAD Copy Link
Total Size	5.6 GB
Total Files	115
Last Seen	2024-10-05 23:36
Hash	0CDBA976D648FBE322133833323491EBF8B34340

/01_Week_One-_Basic_Concepts_in_Machine_Learning/
01_Class_Information.mp4	26.5 MB
02_What_Is_Machine_Learning.mp4	40.7 MB
03_Applications_of_Machine_Learning.mp4	41.8 MB
04_Key_Elements_of_Machine_Learning.mp4	80.3 MB
05_Types_of_Learning.mp4	64.3 MB
06_Machine_Learning_in_Practice.mp4	48.7 MB
07_What_Is_Inductive_Learning.mp4	15.7 MB
08_When_Should_You_Use_Inductive_Learning.mp4	29.3 MB
09_The_Essence_of_Inductive_Learning.mp4	103.9 MB
10_A_Framework_for_Studying_Inductive_Learning.mp4	99.1 MB
/02_Week_Two-_Decision_Tree_Induction/
01_Decision_Trees.mp4	43.3 MB
02_What_Can_a_Decision_Tree_Represent.mp4	28.6 MB
03_Growing_a_Decision_Tree.mp4	28.4 MB
04_Accuracy_and_Information_Gain.mp4	90.4 MB
05_Learning_with_Non-Boolean_Features.mp4	26.6 MB
06_The_Parity_Problem.mp4	20.1 MB
07_Learning_with_Many-Valued_Attributes.mp4	23.6 MB
08_Learning_with_Missing_Values.mp4	39.7 MB
09_The_Overfitting_Problem.mp4	50.7 MB
10_Decision_Tree_Pruning.mp4	83.4 MB
11_Post-Pruning_Trees_to_Rules.mp4	99.0 MB
12_Scaling_Up_Decision_Tree_Learning.mp4	29.3 MB
/03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/
01_Rules_vs._Decision_Trees.mp4	70.5 MB
02_Learning_a_Set_of_Rules.mp4	52.9 MB
03_Estimating_Probabilities_from_Small_Samples.mp4	38.2 MB
04_Learning_Rules_for_Multiple_Classes.mp4	23.8 MB
05_First-Order_Rules.mp4	47.3 MB
06_Learning_First-Order_Rules_Using_FOIL.mp4	102.0 MB
07_Induction_as_Inverted_Deduction.mp4	78.2 MB
08_Inverting_Propositional_Resolution.mp4	67.0 MB
09_Inverting_First-Order_Resolution.mp4	90.9 MB
/04_Week_Four-_Instance-Based_Learning/
01_The_K-Nearest_Neighbor_Algorithm.mp4	72.6 MB
02_Theoretical_Guarantees_on_k-NN.mp4	45.3 MB
03_Distance-Weighted_k-NN.mp4	12.6 MB
04_The_Curse_of_Dimensionality.mp4	61.5 MB
05_Feature_Selection_and_Weighting.mp4	50.1 MB
06_Reducing_the_Computational_Cost_of_k-NN.mp4	46.9 MB
07_Avoiding_Overfitting_in_k-NN.mp4	27.4 MB
08_Locally_Weighted_Regression.mp4	21.0 MB
09_Radial_Basis_Function_Networks.mp4	14.0 MB
10_Case-Based_Reasoning.mp4	16.8 MB
11_Lazy_vs._Eager_Learning.mp4	11.9 MB
12_Collaborative_Filtering.mp4	74.0 MB
/05_Week_Five-_Statistical_Learning/
01_Bayesian_Methods.mp4	21.5 MB
02_Bayes_Theorem_and_MAP_Hypotheses.mp4	107.3 MB
03_Basic_Probability_Formulas.mp4	25.2 MB
04_MAP_Learning.mp4	60.5 MB
05_Learning_a_Real-Valued_Function.mp4	45.7 MB
06_Bayes_Optimal_Classifier_and_Gibbs_Classifier.mp4	42.4 MB
07_The_Naive_Bayes_Classifier.mp4	107.4 MB
08_Text_Classification.mp4	45.1 MB
09_Bayesian_Networks.mp4	97.6 MB
10_Inference_in_Bayesian_Networks.mp4	16.2 MB
11_Bayesian_Network_Review.mp4	17.2 MB
12_Learning_Bayesian_Networks.mp4	16.1 MB
13_The_EM_Algorithm.mp4	56.5 MB
14_Example_of_EM.mp4	57.9 MB
15_Learning_Bayesian_Network_Structure.mp4	75.0 MB
16_The_Structural_EM_Algorithm.mp4	300.3 MB
/06_Week_Six-_Neural_Networks/
01_Reverse-Engineering_the_Brain.mp4	55.3 MB
02_Neural_Network_Driving_a_Car.mp4	48.9 MB
03_How_Neurons_Work.mp4	36.2 MB
04_The_Perceptron.mp4	53.4 MB
05_Perceptron_Training.mp4	51.0 MB
06_Gradient_Descent.mp4	38.6 MB
07_Gradient_Descent_Continued.mp4	39.2 MB
08_Gradient_Descent_vs._Perceptron_Training.mp4	25.9 MB
09_Stochastic_Gradient_Descent.mp4	19.1 MB
10_Multilayer_Perceptrons.mp4	64.8 MB
11_Backpropagation.mp4	85.9 MB
12_Issues_in_Backpropagation.mp4	105.5 MB
13_Learning_Hidden_Layer_Representations.mp4	59.9 MB
14_Expressiveness_of_Neural_Networks.mp4	30.9 MB
15_Avoiding_Overfitting_in_Neural_Networks.mp4	39.7 MB
/07_Week_Seven-_Model_Ensembles/
01_Model_Ensembles.mp4	14.0 MB
02_Bagging.mp4	39.8 MB
03_Boosting-_The_Basics.mp4	35.9 MB
04_Boosting-_The_Details.mp4	51.8 MB
05_Error-Correcting_Output_Coding.mp4	41.3 MB
06_Stacking.mp4	44.3 MB
/08_Week_Eight-_Learning_Theory/
01_Learning_Theory.mp4	13.4 MB
02_No_Free_Lunch_Theorems.mp4	62.8 MB
03_Practical_Consequences_of_No_Free_Lunch.mp4	36.7 MB
04_Bias_and_Variance.mp4	80.9 MB
05_Bias-Variance_Decomposition_for_Squared_Loss.mp4	16.6 MB
06_General_Bias-Variance_Decomposition.mp4	46.0 MB
07_Bias-Variance_Decomposition_for_Zero-One_Loss.mp4	26.8 MB
08_Bias_and_Variance_for_Other_Loss_Functions.mp4	16.6 MB
09_PAC_Learning.mp4	42.0 MB
10_How_Many_Examples_Are_Enough.mp4	57.7 MB
11_Examples_and_Definition_of_PAC_Learning.mp4	18.2 MB
12_Agnostic_Learning.mp4	48.0 MB
13_VC_Dimension.mp4	41.9 MB
14_VC_Dimension_of_Hyperplanes.mp4	41.2 MB
15_Sample_Complexity_from_VC_Dimension.mp4	8.1 MB
/09_Week_Nine-_Support_Vector_Machines/
01_Support_Vector_Machines.mp4	32.3 MB
02_Perceptrons_as_Instance-Based_Learning.mp4	54.3 MB
03_Kernels.mp4	70.8 MB
04_Learning_SVMs.mp4	67.9 MB
05_Constrained_Optimization.mp4	78.9 MB
06_Optimization_with_Inequality_Constraints.mp4	55.4 MB
07_The_SMO_Algorithm.mp4	25.4 MB
08_Handling_Noisy_Data_in_SVMs.mp4	57.8 MB
09_Generalization_Bounds_for_SVMs.mp4	43.3 MB
/10_Week_Ten-_Clustering_and_Dimensionality_Reduction/
01_Clustering_and_Dimensionality_Reduction.mp4	35.7 MB
02_K-Means_Clustering.mp4	46.5 MB
03_Mixture_Models.mp4	55.6 MB
04_Mixtures_of_Gaussians.mp4	21.8 MB
05_EM_Algorithm_for_Mixtures_of_Gaussians.mp4	45.4 MB
06_Mixture_Models_vs._K-Means_vs._Bayesian_Networks.mp4	29.3 MB
07_Hierarchical_Clustering.mp4	20.6 MB
08_Principal_Components_Analysis.mp4	61.1 MB
09_Multidimensional_Scaling.mp4	29.7 MB
10_Nonlinear_Dimensionality_Reduction.mp4	47.8 MB
/
entered_login.html	1.4 MB
Total files 115