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

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01_Week_One-_Basic_Concepts_in_Machine_Learning/01_Class_Information.mp426.49MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/02_What_Is_Machine_Learning.mp440.74MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/03_Applications_of_Machine_Learning.mp441.84MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/04_Key_Elements_of_Machine_Learning.mp480.28MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/05_Types_of_Learning.mp464.32MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/06_Machine_Learning_in_Practice.mp448.72MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/07_What_Is_Inductive_Learning.mp415.66MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/08_When_Should_You_Use_Inductive_Learning.mp429.27MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/09_The_Essence_of_Inductive_Learning.mp4103.89MB
01_Week_One-_Basic_Concepts_in_Machine_Learning/10_A_Framework_for_Studying_Inductive_Learning.mp499.12MB
02_Week_Two-_Decision_Tree_Induction/01_Decision_Trees.mp443.30MB
02_Week_Two-_Decision_Tree_Induction/02_What_Can_a_Decision_Tree_Represent.mp428.56MB
02_Week_Two-_Decision_Tree_Induction/03_Growing_a_Decision_Tree.mp428.45MB
02_Week_Two-_Decision_Tree_Induction/04_Accuracy_and_Information_Gain.mp490.38MB
02_Week_Two-_Decision_Tree_Induction/05_Learning_with_Non-Boolean_Features.mp426.59MB
02_Week_Two-_Decision_Tree_Induction/06_The_Parity_Problem.mp420.07MB
02_Week_Two-_Decision_Tree_Induction/07_Learning_with_Many-Valued_Attributes.mp423.62MB
02_Week_Two-_Decision_Tree_Induction/08_Learning_with_Missing_Values.mp439.70MB
02_Week_Two-_Decision_Tree_Induction/09_The_Overfitting_Problem.mp450.68MB
02_Week_Two-_Decision_Tree_Induction/10_Decision_Tree_Pruning.mp483.37MB
02_Week_Two-_Decision_Tree_Induction/11_Post-Pruning_Trees_to_Rules.mp498.99MB
02_Week_Two-_Decision_Tree_Induction/12_Scaling_Up_Decision_Tree_Learning.mp429.30MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/01_Rules_vs._Decision_Trees.mp470.48MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/02_Learning_a_Set_of_Rules.mp452.86MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/03_Estimating_Probabilities_from_Small_Samples.mp438.22MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/04_Learning_Rules_for_Multiple_Classes.mp423.80MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/05_First-Order_Rules.mp447.31MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/06_Learning_First-Order_Rules_Using_FOIL.mp4102.01MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/07_Induction_as_Inverted_Deduction.mp478.17MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/08_Inverting_Propositional_Resolution.mp467.00MB
03_Week_Three-_Learning_Sets_of_Rules_and_Logic_Programs/09_Inverting_First-Order_Resolution.mp490.90MB
04_Week_Four-_Instance-Based_Learning/01_The_K-Nearest_Neighbor_Algorithm.mp472.58MB
04_Week_Four-_Instance-Based_Learning/02_Theoretical_Guarantees_on_k-NN.mp445.34MB
04_Week_Four-_Instance-Based_Learning/03_Distance-Weighted_k-NN.mp412.63MB
04_Week_Four-_Instance-Based_Learning/04_The_Curse_of_Dimensionality.mp461.50MB
04_Week_Four-_Instance-Based_Learning/05_Feature_Selection_and_Weighting.mp450.11MB
04_Week_Four-_Instance-Based_Learning/06_Reducing_the_Computational_Cost_of_k-NN.mp446.94MB
04_Week_Four-_Instance-Based_Learning/07_Avoiding_Overfitting_in_k-NN.mp427.44MB
04_Week_Four-_Instance-Based_Learning/08_Locally_Weighted_Regression.mp421.00MB
04_Week_Four-_Instance-Based_Learning/09_Radial_Basis_Function_Networks.mp413.99MB
04_Week_Four-_Instance-Based_Learning/10_Case-Based_Reasoning.mp416.82MB
04_Week_Four-_Instance-Based_Learning/11_Lazy_vs._Eager_Learning.mp411.87MB
04_Week_Four-_Instance-Based_Learning/12_Collaborative_Filtering.mp473.96MB
05_Week_Five-_Statistical_Learning/01_Bayesian_Methods.mp421.47MB
05_Week_Five-_Statistical_Learning/02_Bayes_Theorem_and_MAP_Hypotheses.mp4107.30MB
05_Week_Five-_Statistical_Learning/03_Basic_Probability_Formulas.mp425.20MB
05_Week_Five-_Statistical_Learning/04_MAP_Learning.mp460.52MB
05_Week_Five-_Statistical_Learning/05_Learning_a_Real-Valued_Function.mp445.66MB
05_Week_Five-_Statistical_Learning/06_Bayes_Optimal_Classifier_and_Gibbs_Classifier.mp442.36MB
05_Week_Five-_Statistical_Learning/07_The_Naive_Bayes_Classifier.mp4107.41MB
05_Week_Five-_Statistical_Learning/08_Text_Classification.mp445.07MB
05_Week_Five-_Statistical_Learning/09_Bayesian_Networks.mp497.59MB
05_Week_Five-_Statistical_Learning/10_Inference_in_Bayesian_Networks.mp416.18MB
05_Week_Five-_Statistical_Learning/11_Bayesian_Network_Review.mp417.25MB
05_Week_Five-_Statistical_Learning/12_Learning_Bayesian_Networks.mp416.13MB
05_Week_Five-_Statistical_Learning/13_The_EM_Algorithm.mp456.54MB
05_Week_Five-_Statistical_Learning/14_Example_of_EM.mp457.94MB
05_Week_Five-_Statistical_Learning/15_Learning_Bayesian_Network_Structure.mp474.96MB
05_Week_Five-_Statistical_Learning/16_The_Structural_EM_Algorithm.mp4300.27MB
06_Week_Six-_Neural_Networks/01_Reverse-Engineering_the_Brain.mp455.26MB
06_Week_Six-_Neural_Networks/02_Neural_Network_Driving_a_Car.mp448.93MB
06_Week_Six-_Neural_Networks/03_How_Neurons_Work.mp436.20MB
06_Week_Six-_Neural_Networks/04_The_Perceptron.mp453.41MB
06_Week_Six-_Neural_Networks/05_Perceptron_Training.mp450.97MB
06_Week_Six-_Neural_Networks/06_Gradient_Descent.mp438.55MB
06_Week_Six-_Neural_Networks/07_Gradient_Descent_Continued.mp439.22MB
06_Week_Six-_Neural_Networks/08_Gradient_Descent_vs._Perceptron_Training.mp425.92MB
06_Week_Six-_Neural_Networks/09_Stochastic_Gradient_Descent.mp419.08MB
06_Week_Six-_Neural_Networks/10_Multilayer_Perceptrons.mp464.83MB
06_Week_Six-_Neural_Networks/11_Backpropagation.mp485.93MB
06_Week_Six-_Neural_Networks/12_Issues_in_Backpropagation.mp4105.54MB
06_Week_Six-_Neural_Networks/13_Learning_Hidden_Layer_Representations.mp459.93MB
06_Week_Six-_Neural_Networks/14_Expressiveness_of_Neural_Networks.mp430.87MB
06_Week_Six-_Neural_Networks/15_Avoiding_Overfitting_in_Neural_Networks.mp439.67MB
07_Week_Seven-_Model_Ensembles/01_Model_Ensembles.mp414.00MB
07_Week_Seven-_Model_Ensembles/02_Bagging.mp439.85MB
07_Week_Seven-_Model_Ensembles/03_Boosting-_The_Basics.mp435.88MB
07_Week_Seven-_Model_Ensembles/04_Boosting-_The_Details.mp451.78MB
07_Week_Seven-_Model_Ensembles/05_Error-Correcting_Output_Coding.mp441.27MB
07_Week_Seven-_Model_Ensembles/06_Stacking.mp444.32MB
08_Week_Eight-_Learning_Theory/01_Learning_Theory.mp413.42MB
08_Week_Eight-_Learning_Theory/02_No_Free_Lunch_Theorems.mp462.80MB
08_Week_Eight-_Learning_Theory/03_Practical_Consequences_of_No_Free_Lunch.mp436.67MB
08_Week_Eight-_Learning_Theory/04_Bias_and_Variance.mp480.92MB
08_Week_Eight-_Learning_Theory/05_Bias-Variance_Decomposition_for_Squared_Loss.mp416.62MB
08_Week_Eight-_Learning_Theory/06_General_Bias-Variance_Decomposition.mp446.04MB
08_Week_Eight-_Learning_Theory/07_Bias-Variance_Decomposition_for_Zero-One_Loss.mp426.84MB
08_Week_Eight-_Learning_Theory/08_Bias_and_Variance_for_Other_Loss_Functions.mp416.60MB
08_Week_Eight-_Learning_Theory/09_PAC_Learning.mp441.98MB
08_Week_Eight-_Learning_Theory/10_How_Many_Examples_Are_Enough.mp457.66MB
08_Week_Eight-_Learning_Theory/11_Examples_and_Definition_of_PAC_Learning.mp418.18MB
08_Week_Eight-_Learning_Theory/12_Agnostic_Learning.mp447.99MB
08_Week_Eight-_Learning_Theory/13_VC_Dimension.mp441.91MB
08_Week_Eight-_Learning_Theory/14_VC_Dimension_of_Hyperplanes.mp441.18MB
08_Week_Eight-_Learning_Theory/15_Sample_Complexity_from_VC_Dimension.mp48.09MB
09_Week_Nine-_Support_Vector_Machines/01_Support_Vector_Machines.mp432.32MB
09_Week_Nine-_Support_Vector_Machines/02_Perceptrons_as_Instance-Based_Learning.mp454.29MB
09_Week_Nine-_Support_Vector_Machines/03_Kernels.mp470.78MB
09_Week_Nine-_Support_Vector_Machines/04_Learning_SVMs.mp467.90MB
09_Week_Nine-_Support_Vector_Machines/05_Constrained_Optimization.mp478.89MB
09_Week_Nine-_Support_Vector_Machines/06_Optimization_with_Inequality_Constraints.mp455.43MB
09_Week_Nine-_Support_Vector_Machines/07_The_SMO_Algorithm.mp425.36MB
09_Week_Nine-_Support_Vector_Machines/08_Handling_Noisy_Data_in_SVMs.mp457.78MB
09_Week_Nine-_Support_Vector_Machines/09_Generalization_Bounds_for_SVMs.mp443.28MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/01_Clustering_and_Dimensionality_Reduction.mp435.68MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/02_K-Means_Clustering.mp446.52MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/03_Mixture_Models.mp455.59MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/04_Mixtures_of_Gaussians.mp421.76MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/05_EM_Algorithm_for_Mixtures_of_Gaussians.mp445.36MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/06_Mixture_Models_vs._K-Means_vs._Bayesian_Networks.mp429.32MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/07_Hierarchical_Clustering.mp420.62MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/08_Principal_Components_Analysis.mp461.08MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/09_Multidimensional_Scaling.mp429.73MB
10_Week_Ten-_Clustering_and_Dimensionality_Reduction/10_Nonlinear_Dimensionality_Reduction.mp447.82MB
entered_login.html1.37MB
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