2014斯坦福大学机器学习mkv视频

1. 1 - 1 - Welcome (7 min).mkv11.69MB
2. 1 - 2 - What is Machine Learning_ (7 min).mkv9.25MB
3. 1 - 3 - Supervised Learning (12 min).mkv13.25MB
4. 1 - 4 - Unsupervised Learning (14 min).mkv16.45MB
5. 10 - 1 - Deciding What to Try Next (6 min).mkv6.78MB
6. 10 - 2 - Evaluating a Hypothesis (8 min).mkv8.36MB
7. 10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv14.92MB
8. 10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv8.86MB
9. 10 - 5 - Regularization and Bias_Variance (11 min).mkv12.42MB
10. 10 - 6 - Learning Curves (12 min).mkv12.74MB
11. 10 - 7 - Deciding What to Do Next Revisited (7 min).mkv8.08MB
12. 11 - 1 - Prioritizing What to Work On (10 min).mkv11.03MB
13. 11 - 2 - Error Analysis (13 min).mkv15.22MB
14. 11 - 3 - Error Metrics for Skewed Classes (12 min).mkv13.07MB
15. 11 - 4 - Trading Off Precision and Recall (14 min).mkv15.77MB
16. 11 - 5 - Data For Machine Learning (11 min).mkv12.7MB
17. 12 - 1 - Optimization Objective (15 min).mkv16.42MB
18. 12 - 2 - Large Margin Intuition (11 min).mkv11.65MB
19. 12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv21.51MB
20. 12 - 4 - Kernels I (16 min).mkv17.32MB
21. 12 - 5 - Kernels II (16 min).mkv17.2MB
22. 12 - 6 - Using An SVM (21 min).mkv23.63MB
23. 13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv3.76MB
24. 13 - 2 - K-Means Algorithm (13 min).mkv13.61MB
25. 13 - 3 - Optimization Objective (7 min)(1).mkv8.04MB
26. 13 - 3 - Optimization Objective (7 min).mkv8.03MB
27. 13 - 4 - Random Initialization (8 min).mkv8.56MB
28. 13 - 5 - Choosing the Number of Clusters (8 min).mkv9.28MB
29. 14 - 1 - Motivation I_ Data Compression (10 min).mkv14.15MB
30. 14 - 2 - Motivation II_ Visualization (6 min).mkv6.22MB
31. 14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv10.32MB
32. 14 - 4 - Principal Component Analysis Algorithm (15 min).mkv17.55MB
33. 14 - 5 - Choosing the Number of Principal Components (11 min).mkv11.67MB
34. 14 - 6 - Reconstruction from Compressed Representation (4 min).mkv4.92MB
35. 14 - 7 - Advice for Applying PCA (13 min).mkv14.5MB
36. 15 - 1 - Problem Motivation (8 min).mkv8.23MB
37. 15 - 2 - Gaussian Distribution (10 min).mkv11.53MB
38. 15 - 3 - Algorithm (12 min).mkv13.77MB
39. 15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv14.96MB
40. 15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv9.17MB
41. 15 - 6 - Choosing What Features to Use (12 min).mkv13.93MB
42. 15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv15.72MB
43. 15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv16.12MB
44. 16 - 1 - Problem Formulation (8 min).mkv10.57MB
45. 16 - 2 - Content Based Recommendations (15 min).mkv16.71MB
46. 16 - 3 - Collaborative Filtering (10 min).mkv11.6MB
47. 16 - 4 - Collaborative Filtering Algorithm (9 min).mkv10.18MB
48. 16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv9.55MB
49. 16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv9.58MB
50. 17 - 1 - Learning With Large Datasets (6 min).mkv6.41MB
51. 17 - 2 - Stochastic Gradient Descent (13 min).mkv15.12MB
52. 17 - 3 - Mini-Batch Gradient Descent (6 min).mkv7.22MB
53. 17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv13.15MB
54. 17 - 5 - Online Learning (13 min).mkv14.72MB
55. 17 - 6 - Map Reduce and Data Parallelism (14 min).mkv15.84MB
56. 18 - 1 - Problem Description and Pipeline (7 min).mkv7.81MB
57. 18 - 2 - Sliding Windows (15 min).mkv16.3MB
58. 18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv18.57MB
59. 18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv15.9MB
60. 19 - 1 - Summary and Thank You (5 min).mkv6.02MB
61. 2 - 1 - Model Representation (8 min).mkv8.86MB
62. 2 - 2 - Cost Function (8 min).mkv8.91MB
63. 2 - 3 - Cost Function - Intuition I (11 min).mkv12.06MB
64. 2 - 4 - Cost Function - Intuition II (9 min).mkv11.22MB
65. 2 - 5 - Gradient Descent (11 min).mkv13.32MB
66. 2 - 6 - Gradient Descent Intuition (12 min).mkv12.84MB
67. 2 - 7 - GradientDescentForLinearRegression (6 min).mkv12.02MB
68. 2 - 8 - What_'s Next (6 min).mkv5.99MB
69. 3 - 1 - Matrices and Vectors (9 min).mkv9.42MB
70. 3 - 2 - Addition and Scalar Multiplication (7 min).mkv7.35MB
71. 3 - 3 - Matrix Vector Multiplication (14 min).mkv14.78MB
72. 3 - 4 - Matrix Matrix Multiplication (11 min).mkv12.42MB
73. 3 - 5 - Matrix Multiplication Properties (9 min).mkv9.67MB
74. 3 - 6 - Inverse and Transpose (11 min).mkv12.69MB
75. 4 - 1 - Multiple Features (8 min).mkv8.71MB
76. 4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv5.71MB
77. 4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv9.32MB
78. 4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv9.13MB
79. 4 - 5 - Features and Polynomial Regression (8 min).mkv8.15MB
80. 4 - 6 - Normal Equation (16 min).mkv16.88MB
81. 4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv6.15MB
82. 5 - 1 - Basic Operations (14 min).mkv17.5MB
83. 5 - 2 - Moving Data Around (16 min).mkv20.52MB
84. 5 - 3 - Computing on Data (13 min).mkv15.04MB
85. 5 - 4 - Plotting Data (10 min).mkv13.17MB
86. 5 - 5 - Control Statements_ for, while, if statements (13 min).mkv16.29MB
87. 5 - 6 - Vectorization (14 min).mkv15.88MB
88. 5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv5.41MB
89. 6 - 1 - Classification (8 min).mkv8.65MB
90. 6 - 2 - Hypothesis Representation (7 min).mkv8.23MB
91. 6 - 3 - Decision Boundary (15 min).mkv16.51MB
92. 6 - 4 - Cost Function (11 min).mkv12.92MB
93. 6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv11.8MB
94. 6 - 6 - Advanced Optimization (14 min).mkv17.95MB
95. 6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv6.83MB
96. 7 - 1 - The Problem of Overfitting (10 min).mkv11MB
97. 7 - 2 - Cost Function (10 min).mkv11.48MB
98. 7 - 3 - Regularized Linear Regression (11 min).mkv11.84MB
99. 7 - 4 - Regularized Logistic Regression (9 min).mkv10.77MB
100. 8 - 1 - Non-linear Hypotheses (10 min).mkv10.73MB
101. 8 - 2 - Neurons and the Brain (8 min).mkv9.77MB
102. 8 - 3 - Model Representation I (12 min).mkv13.32MB
103. 8 - 4 - Model Representation II (12 min).mkv13.27MB
104. 8 - 5 - Examples and Intuitions I (7 min).mkv7.78MB
105. 8 - 6 - Examples and Intuitions II (10 min).mkv13.84MB
106. 8 - 7 - Multiclass Classification (4 min).mkv4.77MB
107. 9 - 1 - Cost Function (7 min).mkv7.56MB
108. 9 - 2 - Backpropagation Algorithm (12 min).mkv13.75MB
109. 9 - 3 - Backpropagation Intuition (13 min).mkv15.25MB
110. 9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv9.27MB
111. 9 - 5 - Gradient Checking (12 min).mkv13.32MB
112. 9 - 6 - Random Initialization (7 min).mkv7.46MB
113. 9 - 7 - Putting It Together (14 min).mkv16.1MB
114. 9 - 8 - Autonomous Driving (7 min).mkv14.79MB
115. 教程和笔记/Stanford-Machine-Learning-Course-master/DecisionTrees &Boosting/noisy.dat2.06KB
116. 教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_no_momentum.data469.53KB
117. 教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_small.data159B
118. 教程和笔记/Stanford-Machine-Learning-Course-master/HMM/robot_with_momentum.data469.53KB
119. 教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_all.data397.22KB
120. 教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_bos_la.data197.79KB
121. 教程和笔记/Stanford-Machine-Learning-Course-master/HMM/weather_bos_sea.data208.69KB
122. 教程和笔记/Stanford-Machine-Learning-Course-master/K-Means Clustering and PCA/mlclass-ex7/plotDataPoints.m434B
123. 教程和笔记/Stanford-Machine-Learning-Course-master/Neural network learning/mlclass-ex4/checkNNGradients.m1.9KB
124. 教程和笔记/Stanford-Machine-Learning-Course-master/Neural network learning/mlclass-ex4/debugInitializeWeights.m841B
125. 教程和笔记/Stanford-Machine-Learning-Course-master/Neural network learning/mlclass-ex4/randInitializeWeights.m982B
126. 机器学习课程2014源代码/mlclass-ex4-jin/checkNNGradients.m1.9KB
127. 机器学习课程2014源代码/mlclass-ex4-jin/debugInitializeWeights.m841B
128. 机器学习课程2014源代码/mlclass-ex4-jin/randInitializeWeights.m980B
129. 机器学习课程2014源代码/mlclass-ex7-jin/plotDataPoints.m434B