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[Coursera] Machine Learning by Andrew Ng

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文件列表

  1. 08. Neural Networks Representation (Week 4)/docs-slides-Lecture8.pptx 40MB
  2. 12. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4 24MB
  3. 12. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4 22MB
  4. 05. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4 21MB
  5. 18. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4 19MB
  6. 06. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4 18MB
  7. 14. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4 18MB
  8. 05. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4 18MB
  9. 12. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4 18MB
  10. 12. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4 17MB
  11. 04. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4 17MB
  12. 16. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4 17MB
  13. 06. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4 17MB
  14. 01. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4 17MB
  15. 12. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4 17MB
  16. 18. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4 17MB
  17. 05. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).mp4 16MB
  18. 15. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4 16MB
  19. 09. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4 16MB
  20. 18. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4 16MB
  21. 05. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4 16MB
  22. 17. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4 16MB
  23. 11. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4 16MB
  24. 15. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4 16MB
  25. 09. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4 15MB
  26. 11. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4 15MB
  27. 17. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4 15MB
  28. 05. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4 15MB
  29. 15. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4 15MB
  30. 03. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4 15MB
  31. 17. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4 15MB
  32. 09. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4 15MB
  33. 14. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4 15MB
  34. 14. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).mp4 14MB
  35. 15. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4 14MB
  36. 10. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4 14MB
  37. 08. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4 14MB
  38. 15. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4 14MB
  39. 09. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4 14MB
  40. 13. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4 14MB
  41. 08. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4 14MB
  42. 02. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4 13MB
  43. 09. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4 13MB
  44. 01. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4 13MB
  45. 08. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4 13MB
  46. 17. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4 13MB
  47. 05. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4 13MB
  48. 11. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4 13MB
  49. 06. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4 13MB
  50. 02. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4 13MB
  51. 10. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp4 13MB
  52. 11. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp4 13MB
  53. 03. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp4 13MB
  54. 10. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias-Variance (11 min).mp4 13MB
  55. 03. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp4 13MB
  56. 02. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp4 12MB
  57. 02. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4 12MB
  58. 07. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp4 12MB
  59. 06. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4 12MB
  60. 01. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp4 12MB
  61. 14. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp4 12MB
  62. 12. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp4 12MB
  63. 16. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp4 12MB
  64. 15. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp4 12MB
  65. 07. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp4 12MB
  66. 02. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp4 11MB
  67. 11. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp4 11MB
  68. 07. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp4 11MB
  69. Programming Assignments/mlclass-ex7-004.zip 11MB
  70. 07. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp4 11MB
  71. 08. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp4 11MB
  72. 16. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp4 11MB
  73. 14. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4 10MB
  74. 16. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4 10MB
  75. 08. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp4 10MB
  76. 03. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp4 10MB
  77. 16. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).mp4 10MB
  78. 16. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp4 10MB
  79. 03. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp4 10MB
  80. 04. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4 9MB
  81. 13. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp4 9MB
  82. 09. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp4 9MB
  83. 01. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).mp4 9MB
  84. 15. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4 9MB
  85. 04. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4 9MB
  86. 02. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp4 9MB
  87. 02. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp4 9MB
  88. 10. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4 9MB
  89. 04. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp4 9MB
  90. 06. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp4 9MB
  91. 13. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp4 9MB
  92. 10. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp4 8MB
  93. 15. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp4 8MB
  94. 06. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp4 8MB
  95. 04. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp4 8MB
  96. 10. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4 8MB
  97. 13. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp4 8MB
  98. 18. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp4 8MB
  99. 08. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp4 8MB
  100. 09. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp4 8MB
  101. Programming Assignments/mlclass-ex4-004.zip 8MB
  102. 09. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp4 8MB
  103. Programming Assignments/mlclass-ex3-004.zip 8MB
  104. 03. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp4 7MB
  105. 17. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4 7MB
  106. 06. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).mp4 7MB
  107. 10. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp4 7MB
  108. 17. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp4 6MB
  109. 14. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).mp4 6MB
  110. 04. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4 6MB
  111. 18. Application Example Photo OCR/docs-slides-Lecture18.pptx 6MB
  112. 19. Conclusion/19 - 1 - Summary and Thank You (5 min).mp4 6MB
  113. 02. Linear Regression with One Variable (Week 1)/2 - 8 - What-'s Next (6 min).mp4 6MB
  114. 15. Anomaly Detection (Week 9)/docs-slides-Lecture15.pptx 6MB
  115. 04. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4 6MB
  116. 05. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4 5MB
  117. 12. Support Vector Machines (Week 7)/docs-slides-Lecture12.pptx 5MB
  118. 02. Linear Regression with One Variable (Week 1)/docs-slides-Lecture2.pptx 5MB
  119. 14. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4 5MB
  120. 08. Neural Networks Representation (Week 4)/docs-slides-Lecture8.pdf 5MB
  121. 09. Neural Networks Learning (Week 5)/docs-slides-Lecture9.pptx 5MB
  122. 03. Linear Algebra Review (Week 1, Optional)/docs-slides-Lecture3.pptx 5MB
  123. 08. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp4 5MB
  124. 04. Linear Regression with Multiple Variables (Week 2)/docs-slides-Lecture4.pptx 4MB
  125. 01. Introduction (Week 1)/docs-slides-Lecture1.pptx 4MB
  126. 06. Logistic Regression (Week 3)/docs-slides-Lecture6.pptx 4MB
  127. 13. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).mp4 4MB
  128. 17. Large Scale Machine Learning (Week 10)/docs-slides-Lecture17.pptx 4MB
  129. 14. Dimensionality Reduction (Week 8)/docs-slides-Lecture14.pptx 4MB
  130. 16. Recommender Systems (Week 9)/docs-slides-Lecture16.pptx 4MB
  131. 09. Neural Networks Learning (Week 5)/docs-slides-Lecture9.pdf 3MB
  132. 10. Advice for Applying Machine Learning (Week 6)/docs-slides-Lecture10.pptx 3MB
  133. 15. Anomaly Detection (Week 9)/docs-slides-Lecture15.pdf 3MB
  134. 01. Introduction (Week 1)/docs-slides-Lecture1.pdf 3MB
  135. 02. Linear Regression with One Variable (Week 1)/docs-slides-Lecture2.pdf 3MB
  136. 13. Clustering (Week 8)/docs-slides-Lecture13.pptx 3MB
  137. 07. Regularization (Week 3)/docs-slides-Lecture7.pptx 3MB
  138. 07. Regularization (Week 3)/docs-slides-Lecture7.pdf 2MB
  139. 12. Support Vector Machines (Week 7)/docs-slides-Lecture12.pdf 2MB
  140. Wiki - Tutoring _ Coursera.pdf 2MB
  141. 13. Clustering (Week 8)/docs-slides-Lecture13.pdf 2MB
  142. 06. Logistic Regression (Week 3)/docs-slides-Lecture6.pdf 2MB
  143. 17. Large Scale Machine Learning (Week 10)/docs-slides-Lecture17.pdf 2MB
  144. 18. Application Example Photo OCR/docs-slides-Lecture18.pdf 2MB
  145. 11. Machine Learning System Design (Week 6)/docs-slides-Lecture11.pptx 2MB
  146. Homeworks/12. Support Vector Machines.pdf 2MB
  147. 03. Linear Algebra Review (Week 1, Optional)/docs-slides-Lecture3.pdf 2MB
  148. 04. Linear Regression with Multiple Variables (Week 2)/docs-slides-Lecture4.pdf 2MB
  149. 14. Dimensionality Reduction (Week 8)/docs-slides-Lecture14.pdf 2MB
  150. 10. Advice for Applying Machine Learning (Week 6)/docs-slides-Lecture10.pdf 1MB
  151. 16. Recommender Systems (Week 9)/docs-slides-Lecture16.pdf 1MB
  152. Homeworks/08. Neural Networks Representation.pdf 1MB
  153. Homeworks/15. Principal Component Analysis.pdf 1MB
  154. Programming Assignments/mlclass-ex6-004.zip 893KB
  155. Wiki - Octave __ Matlab Tutorial _ Coursera.pdf 886KB
  156. Programming Assignments/mlclass-ex8-004.zip 791KB
  157. Homeworks/16. Recommender Systems.pdf 688KB
  158. Homeworks/18. Application Photo OCR.pdf 684KB
  159. Homeworks/06. Logistic Regression.pdf 676KB
  160. Homeworks/05. Octave Tutorial.pdf 645KB
  161. Homeworks/03. Linear Algebra.pdf 643KB
  162. Homeworks/14. Anomaly Detection.pdf 632KB
  163. Homeworks/17. Large Scale Machine Learning.pdf 611KB
  164. Homeworks/07. Regularization.pdf 610KB
  165. Homeworks/09. Neural Networks Learning.pdf 605KB
  166. Homeworks/02. Linear regression with one variable.pdf 605KB
  167. Homeworks/13. Clustering.pdf 578KB
  168. Homeworks/11. Machine Learning System Design.pdf 570KB
  169. Homeworks/04. Linear Regression with Multiple Variables.pdf 566KB
  170. 11. Machine Learning System Design (Week 6)/docs-slides-Lecture11.pdf 498KB
  171. Programming Assignments/mlclass-ex1-004.zip 464KB
  172. 05. Octave Tutorial (Week 2)/docs-slides-Lecture5.pptx 407KB
  173. Homeworks/10. Advice for Applying Machine Learning.pdf 289KB
  174. 05. Octave Tutorial (Week 2)/docs-slides-Lecture5.pdf 242KB
  175. Programming Assignments/mlclass-ex2-004.zip 238KB
  176. Programming Assignments/List Assignments _ Coursera.pdf 193KB
  177. Programming Assignments/mlclass-ex5-004.zip 173KB
  178. Homeworks/View Review Questions _ Coursera.pdf 147KB
  179. Wiki - Course FAQ _ Coursera.pdf 98KB
  180. Homeworks/01. Introduction.pdf 91KB
  181. Wiki - Course Information _ Coursera.pdf 82KB
  182. Wiki - Course Schedule _ Coursera.pdf 70KB
  183. Programming Assignments/Assignment Details _ Coursera.pdf 56KB
  184. small-icon.hover.png 26KB