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林軒田(Hsuan-Tien Lin) - [機器學習基石]Machine Learning Foundations

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2019-2-24 21:03 2024-5-20 07:23 140 2.04 GB 65
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林軒田 Hsuan-Tien Lin - 機器學習基石 Machine Learning Foundations

文件列表
  1. P10 3-1-Learning with Different Output Space @ Machine Learning Foundations (機器學習基石).mp464.54MB
  2. P11 3-2-Learning with Different Data Label @ Machine Learning Foundations (機器學習基石).mp458.8MB
  3. P13 3-4-Learning with Different Input Space @ Machine Learning Foundations (機器學習基石).mp446.5MB
  4. P30 8-1-Noise and Probabilistic Target @ Machine Learning Foundations (機器學習基石).mp445.33MB
  5. P2 1-2-What Is Machine Learning @ Machine Learning Foundations (機器學習基石).mp444.01MB
  6. P46 12-1-Quadratic Hypotheses @ Machine Learning Foundations (機器學習基石).mp443.16MB
  7. P3 1-3-Applications of Machine Learning @ Machine Learning Foundations (機器學習基石).mp442.19MB
  8. P54 14-1-Regularized Hypothesis Set @ Machine Learning Foundations (機器學習基石).mp441.09MB
  9. P55 14-2-Weight Decay Regularization @ Machine Learning Foundations (機器學習基石).mp440.79MB
  10. P19 5-2-Effective Number of Lines @ Machine Learning Foundations (機器學習基石).mp440.69MB
  11. P12 3-3-Learning with Different Protocol @ Machine Learning Foundations (機器學習基石).mp439.81MB
  12. P9 2-4-Non-Separable Data @ Machine Learning Foundations (機器學習基石).mp439.56MB
  13. P58 15-1-Model Selection Problem @ Machine Learning Foundations (機器學習基石).mp438.68MB
  14. P42 11-1-Linear Models for Binary Classification @ Machine Learning Foundations (機器學習基石).mp438.55MB
  15. P18 5-1-Recap and Preview @ Machine Learning Foundations (機器學習基石).mp437.97MB
  16. P16 4-3-Connection to Learning @ Machine Learning Foundations (機器學習基石).mp437.83MB
  17. P17 4-4-Connection to Real Learning @ Machine Learning Foundations (機器學習基石).mp437.7MB
  18. P14 4-1-Learning is Impossible @ Machine Learning Foundations (機器學習基石).mp437.62MB
  19. P41 10-4-Gradient Descent @ Machine Learning Foundations (機器學習基石).mp437.56MB
  20. P52 13-3-Deterministic Noise @ Machine Learning Foundations (機器學習基石).mp437.5MB
  21. P26 7-1-Definition of VC Dimension @ Machine Learning Foundations (機器學習基石).mp436.36MB
  22. P48 12-3-Price of Nonlinear Transform @ Machine Learning Foundations (機器學習基石).mp436.34MB
  23. P36 9-3-Generalization Issue @ Machine Learning Foundations (機器學習基石).mp435.92MB
  24. P63 16-2-Sampling Bias @ Machine Learning Foundations (機器學習基石).mp434.56MB
  25. P25 6-4-A Pictorial Proof @ Machine Learning Foundations (機器學習基石).mp433.9MB
  26. P20 5-3-Effective Number of Hypotheses @ Machine Learning Foundations (機器學習基石).mp433.43MB
  27. P1 1-1-Course Introduction @ Machine Learning Foundations (機器學習基石).mp433.27MB
  28. P51 13-2-The Role of Noise and Data Size @ Machine Learning Foundations (機器學習基石).mp432.91MB
  29. P6 2-1-Perceptron Hypothesis Set @ Machine Learning Foundations (機器學習基石).mp432.7MB
  30. P50 13-1-What is Overfitting @ Machine Learning Foundations (機器學習基石).mp432.4MB
  31. P43 11-2-Stochastic Gradient Descent @ Machine Learning Foundations (機器學習基石).mp432.32MB
  32. P64 16-3-Data Snooping @ Machine Learning Foundations (機器學習基石).mp432.16MB
  33. P4 1-4-Components of Learning @ Machine Learning Foundations (機器學習基石).mp432.12MB
  34. P33 8-4-Weighted Classification @ Machine Learning Foundations (機器學習基石).mp431.98MB
  35. P15 4-2-Probability to the Rescue @ Machine Learning Foundations (機器學習基石).mp431.8MB
  36. P57 14-4-General Regularizers @ Machine Learning Foundations (機器學習基石).mp431.28MB
  37. P22 6-1-Restriction of Break Point @ Machine Learning Foundations (機器學習基石).mp430.46MB
  38. P24 6-3-Bounding Function Inductive Cases @ Machine Learning Foundations (機器學習基石).mp430.07MB
  39. P29 7-4-Interpreting VC Dimension @ Machine Learning Foundations (機器學習基石).mp429.41MB
  40. P61 15-4-V-Fold Cross Validation @ Machine Learning Foundations (機器學習基石).mp428.66MB
  41. P40 10-3-Gradient of Logistic Regression Error @ Machine Learning Foundations (機器學習基石).mp428.14MB
  42. P27 7-2-VC Dimension of Perceptrons @ Machine Learning Foundations (機器學習基石).mp427.26MB
  43. P32 8-3-Algorithmic Error Measure @ Machine Learning Foundations (機器學習基石).mp426.91MB
  44. P35 9-2-Linear Regression Algorithm @ Machine Learning Foundations (機器學習基石).mp426.89MB
  45. P62 16-1-Occam's Razor @ Machine Learning Foundations (機器學習基石).mp426.81MB
  46. P39 10-2-Logistic Regression Error @ Machine Learning Foundations (機器學習基石).mp426.44MB
  47. P34 9-1-Linear Regression Problem @ Machine Learning Foundations (機器學習基石).mp426.37MB
  48. P44 11-3-Multiclass via Logistic Regression @ Machine Learning Foundations (機器學習基石).mp426.27MB
  49. P56 14-3-Regularization and VC Theory @ Machine Learning Foundations (機器學習基石).mp425.96MB
  50. P37 9-4-Linear Regression for Binary Classification @ Machine Learning Foundations (機器學習基石).mp425.92MB
  51. P8 2-3-Guarantee of PLA @ Machine Learning Foundations (機器學習基石).mp425.68MB
  52. P7 2-2-Perceptron Learning Algorithm @ Machine Learning Foundations (機器學習基石).mp425.12MB
  53. P21 5-4-Break Point @ Machine Learning Foundations (機器學習基石).mp423.63MB
  54. P60 15-3-Leave-One-Out Cross Validation @ Machine Learning Foundations (機器學習基石).mp423.35MB
  55. P31 8-2-Error Measure @ Machine Learning Foundations (機器學習基石).mp422.93MB
  56. P53 13-4-Dealing with Overfitting @ Machine Learning Foundations (機器學習基石).mp422.71MB
  57. P38 10-1-Logistic Regression Problem @ Machine Learning Foundations (機器學習基石).mp422.69MB
  58. P59 15-2-Validation @ Machine Learning Foundations (機器學習基石).mp421.76MB
  59. P45 11-4-Multiclass via Binary Classification @ Machine Learning Foundations (機器學習基石).mp421.73MB
  60. P65 16-4-Power of Three @ Machine Learning Foundations (機器學習基石).mp421.1MB
  61. P5 1-5-Machine Learning and Other Fields @ Machine Learning Foundations (機器學習基石).mp419.63MB
  62. P47 12-2-Nonlinear Transform @ Machine Learning Foundations (機器學習基石).mp418.32MB
  63. P49 12-4-Structured Hypothesis Sets @ Machine Learning Foundations (機器學習基石).mp416.91MB
  64. P23 6-2-Bounding Function Basic Cases @ Machine Learning Foundations (機器學習基石).mp416.16MB
  65. P28 7-3-Physical Intuition of VC Dimension @ Machine Learning Foundations (機器學習基石).mp414.61MB
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