| Info hash | 0cdba976d648fbe322133833323491ebf8b34340 |
| Last mirror activity | 20d,20:11:20 ago |
| Size | 5.65GB (5,649,312,721 bytes) |
| Added | 2017-03-05 01:25:12 |
| Views | 2064 |
| Hits | 7760 |
| ID | 3628 |
| Type | multi |
| Downloaded | 7107 time(s) |
| Uploaded by |  pj |
| Folder | machlearning-001 |
| Num files | 115 files [See full list] |
| Mirrors | 14 complete, 0 downloading = 14 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 |
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Type: Course
Tags: Coursera, machlearning
Bibtex:
Tags: Coursera, machlearning
Bibtex:
@article{,
title = {[Coursera] Machine Learning (University of Washington) (machlearning)},
author = {University of Washington}
}
