[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
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Type: Course
Tags: Coursera, machlearning

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

10 day statistics (24 downloads)

Average Time 26 mins, 33 secs
Average Speed 3.54MB/s
Best Time 5 mins, 00 secs
Best Speed 18.83MB/s
Worst Time 2 hrs, 01 mins, 35 secs
Worst Speed 774.41kB/s