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[Coursera] Machine Learning by Andrew Ng

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Coursera Machine Learning by Andrew Ng

文件列表
  1. 01. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp411.95MB
  2. 01. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).mp49.35MB
  3. 01. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp413.45MB
  4. 01. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp416.66MB
  5. 02. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp49MB
  6. 02. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp49.05MB
  7. 02. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp412.24MB
  8. 02. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp411.36MB
  9. 02. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp413.5MB
  10. 02. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp413.03MB
  11. 02. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp412.18MB
  12. 02. Linear Regression with One Variable (Week 1)/2 - 8 - What-'s Next (6 min).mp46.08MB
  13. 03. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp49.56MB
  14. 03. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp47.46MB
  15. 03. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp415MB
  16. 03. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp412.59MB
  17. 03. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp49.81MB
  18. 03. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp412.87MB
  19. 04. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp48.84MB
  20. 04. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp45.78MB
  21. 04. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp49.46MB
  22. 04. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp49.26MB
  23. 04. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp48.26MB
  24. 04. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp417.13MB
  25. 04. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp46.24MB
  26. 05. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp417.72MB
  27. 05. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp420.77MB
  28. 05. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp415.25MB
  29. 05. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp413.32MB
  30. 05. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).mp416.49MB
  31. 05. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp416.09MB
  32. 05. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp45.46MB
  33. 06. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp48.77MB
  34. 06. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp48.34MB
  35. 06. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp416.74MB
  36. 06. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp413.09MB
  37. 06. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp411.96MB
  38. 06. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp418.15MB
  39. 06. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).mp46.93MB
  40. 07. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp411.15MB
  41. 07. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp411.63MB
  42. 07. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp412MB
  43. 07. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp410.89MB
  44. 08. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp410.88MB
  45. 08. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp49.89MB
  46. 08. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp413.51MB
  47. 08. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp413.45MB
  48. 08. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp47.89MB
  49. 08. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp414MB
  50. 08. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp44.83MB
  51. 09. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp47.66MB
  52. 09. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp413.94MB
  53. 09. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp415.44MB
  54. 09. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp49.38MB
  55. 09. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp413.5MB
  56. 09. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp47.56MB
  57. 09. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp416.3MB
  58. 09. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp414.88MB
  59. 10. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp46.86MB
  60. 10. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp48.48MB
  61. 10. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp414.07MB
  62. 10. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp48.97MB
  63. 10. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias-Variance (11 min).mp412.6MB
  64. 10. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp412.92MB
  65. 10. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp48.18MB
  66. 11. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp411.17MB
  67. 11. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp415.43MB
  68. 11. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp413.25MB
  69. 11. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp415.99MB
  70. 11. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp412.87MB
  71. 12. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp416.65MB
  72. 12. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp411.81MB
  73. 12. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp421.83MB
  74. 12. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp417.57MB
  75. 12. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp417.45MB
  76. 12. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp423.95MB
  77. 13. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).mp43.8MB
  78. 13. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp413.81MB
  79. 13. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp48.15MB
  80. 13. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp48.67MB
  81. 13. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp49.4MB
  82. 14. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).mp414.31MB
  83. 14. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).mp46.3MB
  84. 14. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp410.45MB
  85. 14. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp417.79MB
  86. 14. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp411.84MB
  87. 14. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp44.98MB
  88. 14. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp414.7MB
  89. 15. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp48.35MB
  90. 15. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp411.69MB
  91. 15. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp413.95MB
  92. 15. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp415.15MB
  93. 15. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp49.28MB
  94. 15. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp414.12MB
  95. 15. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp415.93MB
  96. 15. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp416.34MB
  97. 16. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp410.67MB
  98. 16. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp416.93MB
  99. 16. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp411.75MB
  100. 16. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp410.31MB
  101. 16. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp49.68MB
  102. 16. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).mp49.71MB
  103. 17. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp46.5MB
  104. 17. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp415.33MB
  105. 17. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp47.32MB
  106. 17. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp413.33MB
  107. 17. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp414.91MB
  108. 17. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp416.06MB
  109. 18. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp47.91MB
  110. 18. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp416.52MB
  111. 18. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp418.82MB
  112. 18. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp416.11MB
  113. 19. Conclusion/19 - 1 - Summary and Thank You (5 min).mp46.09MB
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