[Coursera] Machine Learning (University of Washington) (machlearning)
University of Washington

machlearning-001 (115 files)
01_Week_One-_Basic_Concepts_in_Machine_Learning/01_Class_Information.mp4 26.49MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/02_What_Is_Machine_Learning.mp4 40.74MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/03_Applications_of_Machine_Learning.mp4 41.84MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/04_Key_Elements_of_Machine_Learning.mp4 80.28MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/05_Types_of_Learning.mp4 64.32MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/06_Machine_Learning_in_Practice.mp4 48.72MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/07_What_Is_Inductive_Learning.mp4 15.66MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/08_When_Should_You_Use_Inductive_Learning.mp4 29.27MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/09_The_Essence_of_Inductive_Learning.mp4 103.89MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/10_A_Framework_for_Studying_Inductive_Learning.mp4 99.12MB
02_Week_Two-_Decision_Tree_Induction/01_Decision_Trees.mp4 43.30MB
02_Week_Two-_Decision_Tree_Induction/02_What_Can_a_Decision_Tree_Represent.mp4 28.56MB
02_Week_Two-_Decision_Tree_Induction/03_Growing_a_Decision_Tree.mp4 28.45MB
02_Week_Two-_Decision_Tree_Induction/04_Accuracy_and_Information_Gain.mp4 90.38MB
02_Week_Two-_Decision_Tree_Induction/05_Learning_with_Non-Boolean_Features.mp4 26.59MB
02_Week_Two-_Decision_Tree_Induction/06_The_Parity_Problem.mp4 20.07MB
02_Week_Two-_Decision_Tree_Induction/07_Learning_with_Many-Valued_Attributes.mp4 23.62MB
02_Week_Two-_Decision_Tree_Induction/08_Learning_with_Missing_Values.mp4 39.70MB
02_Week_Two-_Decision_Tree_Induction/09_The_Overfitting_Problem.mp4 50.68MB
02_Week_Two-_Decision_Tree_Induction/10_Decision_Tree_Pruning.mp4 83.37MB
02_Week_Two-_Decision_Tree_Induction/11_Post-Pruning_Trees_to_Rules.mp4 98.99MB
02_Week_Two-_Decision_Tree_Induction/12_Scaling_Up_Decision_Tree_Learning.mp4 29.30MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/01_Rules_vs._Decision_Trees.mp4 70.48MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/02_Learning_a_Set_of_Rules.mp4 52.86MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/03_Estimating_Probabilities_from_Small_Samples.mp4 38.22MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/04_Learning_Rules_for_Multiple_Classes.mp4 23.80MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/05_First-Order_Rules.mp4 47.31MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/06_Learning_First-Order_Rules_Using_FOIL.mp4 102.01MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/07_Induction_as_Inverted_Deduction.mp4 78.17MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/08_Inverting_Propositional_Resolution.mp4 67.00MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/09_Inverting_First-Order_Resolution.mp4 90.90MB
04_Week_Four-_Instance-Based_Learning/01_The_K-Nearest_Neighbor_Algorithm.mp4 72.58MB
04_Week_Four-_Instance-Based_Learning/02_Theoretical_Guarantees_on_k-NN.mp4 45.34MB
04_Week_Four-_Instance-Based_Learning/03_Distance-Weighted_k-NN.mp4 12.63MB
04_Week_Four-_Instance-Based_Learning/04_The_Curse_of_Dimensionality.mp4 61.50MB
04_Week_Four-_Instance-Based_Learning/05_Feature_Selection_and_Weighting.mp4 50.11MB
04_Week_Four-_Instance-Based_Learning/06_Reducing_the_Computational_Cost_of_k-NN.mp4 46.94MB
04_Week_Four-_Instance-Based_Learning/07_Avoiding_Overfitting_in_k-NN.mp4 27.44MB
04_Week_Four-_Instance-Based_Learning/08_Locally_Weighted_Regression.mp4 21.00MB
04_Week_Four-_Instance-Based_Learning/09_Radial_Basis_Function_Networks.mp4 13.99MB
04_Week_Four-_Instance-Based_Learning/10_Case-Based_Reasoning.mp4 16.82MB
04_Week_Four-_Instance-Based_Learning/11_Lazy_vs._Eager_Learning.mp4 11.87MB
04_Week_Four-_Instance-Based_Learning/12_Collaborative_Filtering.mp4 73.96MB
05_Week_Five-_Statistical_Learning/01_Bayesian_Methods.mp4 21.47MB
05_Week_Five-_Statistical_Learning/02_Bayes_Theorem_and_MAP_Hypotheses.mp4 107.30MB
05_Week_Five-_Statistical_Learning/03_Basic_Probability_Formulas.mp4 25.20MB
05_Week_Five-_Statistical_Learning/04_MAP_Learning.mp4 60.52MB
05_Week_Five-_Statistical_Learning/05_Learning_a_Real-Valued_Function.mp4 45.66MB
05_Week_Five-_Statistical_Learning/06_Bayes_Optimal_Classifier_and_Gibbs_Classifier.mp4 42.36MB
05_Week_Five-_Statistical_Learning/07_The_Naive_Bayes_Classifier.mp4 107.41MB
05_Week_Five-_Statistical_Learning/08_Text_Classification.mp4 45.07MB
05_Week_Five-_Statistical_Learning/09_Bayesian_Networks.mp4 97.59MB
05_Week_Five-_Statistical_Learning/10_Inference_in_Bayesian_Networks.mp4 16.18MB
05_Week_Five-_Statistical_Learning/11_Bayesian_Network_Review.mp4 17.25MB
05_Week_Five-_Statistical_Learning/12_Learning_Bayesian_Networks.mp4 16.13MB
05_Week_Five-_Statistical_Learning/13_The_EM_Algorithm.mp4 56.54MB
05_Week_Five-_Statistical_Learning/14_Example_of_EM.mp4 57.94MB
05_Week_Five-_Statistical_Learning/15_Learning_Bayesian_Network_Structure.mp4 74.96MB
05_Week_Five-_Statistical_Learning/16_The_Structural_EM_Algorithm.mp4 300.27MB
06_Week_Six-_Neural_Networks/01_Reverse-Engineering_the_Brain.mp4 55.26MB
06_Week_Six-_Neural_Networks/02_Neural_Network_Driving_a_Car.mp4 48.93MB
06_Week_Six-_Neural_Networks/03_How_Neurons_Work.mp4 36.20MB
06_Week_Six-_Neural_Networks/04_The_Perceptron.mp4 53.41MB
06_Week_Six-_Neural_Networks/05_Perceptron_Training.mp4 50.97MB
06_Week_Six-_Neural_Networks/06_Gradient_Descent.mp4 38.55MB
06_Week_Six-_Neural_Networks/07_Gradient_Descent_Continued.mp4 39.22MB
06_Week_Six-_Neural_Networks/08_Gradient_Descent_vs._Perceptron_Training.mp4 25.92MB
06_Week_Six-_Neural_Networks/09_Stochastic_Gradient_Descent.mp4 19.08MB
06_Week_Six-_Neural_Networks/10_Multilayer_Perceptrons.mp4 64.83MB
06_Week_Six-_Neural_Networks/11_Backpropagation.mp4 85.93MB
06_Week_Six-_Neural_Networks/12_Issues_in_Backpropagation.mp4 105.54MB
06_Week_Six-_Neural_Networks/13_Learning_Hidden_Layer_Representations.mp4 59.93MB
06_Week_Six-_Neural_Networks/14_Expressiveness_of_Neural_Networks.mp4 30.87MB
06_Week_Six-_Neural_Networks/15_Avoiding_Overfitting_in_Neural_Networks.mp4 39.67MB
07_Week_Seven-_Model_Ensembles/01_Model_Ensembles.mp4 14.00MB
07_Week_Seven-_Model_Ensembles/02_Bagging.mp4 39.85MB
07_Week_Seven-_Model_Ensembles/03_Boosting-_The_Basics.mp4 35.88MB
07_Week_Seven-_Model_Ensembles/04_Boosting-_The_Details.mp4 51.78MB
07_Week_Seven-_Model_Ensembles/05_Error-Correcting_Output_Coding.mp4 41.27MB
07_Week_Seven-_Model_Ensembles/06_Stacking.mp4 44.32MB
08_Week_Eight-_Learning_Theory/01_Learning_Theory.mp4 13.42MB
08_Week_Eight-_Learning_Theory/02_No_Free_Lunch_Theorems.mp4 62.80MB
08_Week_Eight-_Learning_Theory/03_Practical_Consequences_of_No_Free_Lunch.mp4 36.67MB
08_Week_Eight-_Learning_Theory/04_Bias_and_Variance.mp4 80.92MB
08_Week_Eight-_Learning_Theory/05_Bias-Variance_Decomposition_for_Squared_Loss.mp4 16.62MB
08_Week_Eight-_Learning_Theory/06_General_Bias-Variance_Decomposition.mp4 46.04MB
08_Week_Eight-_Learning_Theory/07_Bias-Variance_Decomposition_for_Zero-One_Loss.mp4 26.84MB
08_Week_Eight-_Learning_Theory/08_Bias_and_Variance_for_Other_Loss_Functions.mp4 16.60MB
08_Week_Eight-_Learning_Theory/09_PAC_Learning.mp4 41.98MB
08_Week_Eight-_Learning_Theory/10_How_Many_Examples_Are_Enough.mp4 57.66MB
08_Week_Eight-_Learning_Theory/11_Examples_and_Definition_of_PAC_Learning.mp4 18.18MB
08_Week_Eight-_Learning_Theory/12_Agnostic_Learning.mp4 47.99MB
08_Week_Eight-_Learning_Theory/13_VC_Dimension.mp4 41.91MB
08_Week_Eight-_Learning_Theory/14_VC_Dimension_of_Hyperplanes.mp4 41.18MB
08_Week_Eight-_Learning_Theory/15_Sample_Complexity_from_VC_Dimension.mp4 8.09MB
09_Week_Nine-_Support_Vector_Machines/01_Support_Vector_Machines.mp4 32.32MB
09_Week_Nine-_Support_Vector_Machines/02_Perceptrons_as_Instance-Based_Learning.mp4 54.29MB
09_Week_Nine-_Support_Vector_Machines/03_Kernels.mp4 70.78MB
09_Week_Nine-_Support_Vector_Machines/04_Learning_SVMs.mp4 67.90MB
09_Week_Nine-_Support_Vector_Machines/05_Constrained_Optimization.mp4 78.89MB
09_Week_Nine-_Support_Vector_Machines/06_Optimization_with_Inequality_Constraints.mp4 55.43MB
09_Week_Nine-_Support_Vector_Machines/07_The_SMO_Algorithm.mp4 25.36MB
09_Week_Nine-_Support_Vector_Machines/08_Handling_Noisy_Data_in_SVMs.mp4 57.78MB
09_Week_Nine-_Support_Vector_Machines/09_Generalization_Bounds_for_SVMs.mp4 43.28MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/01_Clustering_and_Dimensionality_Reduction.mp4 35.68MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/02_K-Means_Clustering.mp4 46.52MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/03_Mixture_Models.mp4 55.59MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/04_Mixtures_of_Gaussians.mp4 21.76MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/05_EM_Algorithm_for_Mixtures_of_Gaussians.mp4 45.36MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/06_Mixture_Models_vs._K-Means_vs._Bayesian_Networks.mp4 29.32MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/07_Hierarchical_Clustering.mp4 20.62MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/08_Principal_Components_Analysis.mp4 61.08MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/09_Multidimensional_Scaling.mp4 29.73MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/10_Nonlinear_Dimensionality_Reduction.mp4 47.82MB
entered_login.html 1.37MB
Type: Course
Tags:Coursera, machlearning

Bibtex:
@article{,
    title = {[Coursera] Machine Learning (University of Washington) (machlearning)},
    author = {University of Washington}
    }


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Average Speed 1.37MB/s
Best Time 7 minutes, 28 seconds
Best Speed 12.61MB/s
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