| Info hash | 0cdba976d648fbe322133833323491ebf8b34340 |
| Last mirror activity | 3:28 ago |
| Size | 5.65GB (5,649,312,721 bytes) |
| Added | 2017-03-05 01:25:12 |
| Views | 1813 |
| Hits | 7370 |
| ID | 3628 |
| Type | multi |
| Downloaded | 6057 time(s) |
| Uploaded by |  pj |
| Folder | machlearning-001 |
| Num files | 115 files [See full list] |
| Mirrors | 16 complete, 1 downloading = 17 mirror(s) total [Log in to see full list] |
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 |
Type: Course
Tags: Coursera, machlearning
Bibtex:
Tags: Coursera, machlearning
Bibtex:
@article{,
title = {[Coursera] Machine Learning (University of Washington) (machlearning)},
author = {University of Washington}
}
