Machine Learning

Machine Learning

Machine Learning- Balaraman Ravindran via YouTube Direct link

Week 6 Lecture 34 Regression Trees

35 of 88

35 of 88

Week 6 Lecture 34 Regression Trees

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Classroom Contents

Machine Learning

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  1. 1 Introduction to Machine Learning
  2. 2 Week 1 - Lecture 1 - Introduction to Machine Learning
  3. 3 Week 1 Lecture 2 - Supervised Learning
  4. 4 Week 1 Lecture 3 - Unsupervised Learning
  5. 5 Week 1 Lecture 4 - Reinforcement Learning
  6. 6 Week 2 Lecture 5 - Statistical Decision Theory - Regression
  7. 7 Week 2 Lecture 6 - Statistical Decision Theory - Classification
  8. 8 Week 2 Lecture 7 - Bias - Variance
  9. 9 Week 2 Lecture 8 - Linear Regression
  10. 10 Week 2 Lecture 9 - Multivariate Regression
  11. 11 Week 3 Lecture 10 Subset Selection 1
  12. 12 Week 3 Lecture 11 Subset Selection 2
  13. 13 Week 3 Lecture 12 Shrinkage Methods
  14. 14 Week 3 Lecture 13 Principal Components Regression
  15. 15 Week 3 Lecture 14 Partial Least Squares
  16. 16 Week 3 Lecture 15 Linear Classification
  17. 17 Week 3 Lecture 16 Logistic Regression
  18. 18 Week 3 Lecture 17 Linear Discriminant Analysis 1
  19. 19 Week 3 Lecture 18 Linear Discriminant Analysis 2
  20. 20 Week 3 Lecture 19 Linear Discriminant Analysis 3
  21. 21 Week 4 Lecture 20 Perceptron Learning
  22. 22 Week 4 Lecture 21 SVM - Formulation
  23. 23 Week 4 Lecture 22 SVM - Interpretation & Analysis
  24. 24 Week 4 Lecture 23 SVMs for Linearly Non Separable Data
  25. 25 Week 4 Lecture 24 SVM Kernels
  26. 26 Week 4 Lecture 25 SVM - Hinge Loss Formulation
  27. 27 Week 5 Lecture 26 ANN I - Early Models
  28. 28 Week 5 Lecture 27 ANN II - Backprogpogation I
  29. 29 Week 5 Lecture 28 ANN III - Backpropogation II
  30. 30 Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
  31. 31 MAXIMUM LIKELIHOOD ESTIMATE
  32. 32 Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
  33. 33 Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
  34. 34 Week 6 Lecture 33 Decision Trees - Introduction
  35. 35 Week 6 Lecture 34 Regression Trees
  36. 36 Week 6 Lecture 35 Stopping Criteria & Pruning
  37. 37 Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
  38. 38 Week 6 Lecture 37 Decision Trees - Categorical Attributes
  39. 39 Week 6 Lecture 38 Decision Trees - Multiway Splits
  40. 40 Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits
  41. 41 Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees
  42. 42 Week 6 Lecture 41 Decision Trees - Example
  43. 43 Week 6 Lecture 42 Evaluation Measures 1
  44. 44 Week 6 Lecture 43 Bootstrapping & Cross Validation
  45. 45 Week 6 Lecture 44 - 2 Class Evaluation Measures
  46. 46 Week 6 Lecture 45 - The ROC Curve
  47. 47 Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis
  48. 48 Week 7 Lecture 47 - Introduction to Hypothesis Testing
  49. 49 Week 7 Lecture 48 - Basic Concepts
  50. 50 Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test
  51. 51 Week 7 Lecture 50 -STUDENT'S T-TEST
  52. 52 Week 7 Lecture 51 - Hypothesis Testing IV - The Two Sample and Paired Sample t-tests
  53. 53 Week 7 Lecture 52 - Confidence Intervals
  54. 54 Week 8 Lecture 53 - Ensemble Methods - Bagging, Committee Machines and Stacking
  55. 55 Week 8 Lecture 54 - Boosting
  56. 56 Week 8 Lecture 55 - Gradient Boosting
  57. 57 Week 8 Lecture 56 - Random Forests
  58. 58 Week 8 Lecture 57 - Naive Bayes
  59. 59 Week 9 Lecture 58 Bayesian Networks
  60. 60 Week 9 Lecture 59 Undirected Graphical Models - Introduction
  61. 61 Week 8 Lecture 60 Undirected Graphical Models - Potential Functions
  62. 62 Week 9 Lecture 61 Hidden Markov Models
  63. 63 Week 9 Lecture 62 Variable Elimination
  64. 64 Week 9 Lecture 63 Belief Propagation
  65. 65 Lecture 64 Multi-class Classification
  66. 66 Week 10 Lecture 65 Partional Clustering
  67. 67 Week 10 Lecture 66 Hierarchical Clustering
  68. 68 Week 10 Lecture 67 Threshold Graphs
  69. 69 Week 10 Lecture 68 The BIRCH Algorithm
  70. 70 Week 10 Lecture 69 The CURE Algorithm
  71. 71 Week 10 Lecture 70 Density Based Clustering
  72. 72 Week 11 Lecture 71 Gaussian Mixture Models
  73. 73 Week 11 Lecture 72 Expectation Maximization
  74. 74 Week 11 Lecture 73 Expectation Maximization Continued
  75. 75 Lecture 76 Spectral Clustering
  76. 76 The Apriori Property
  77. 77 Frequent Itemset Mining
  78. 78 Lecture 79 Learning Theory
  79. 79 Lecture 80 Introduction to Reinforcement Learning
  80. 80 Lecture 81 - RL Framework and TD Learning
  81. 81 Lecture 82 Solution Methods & Applications
  82. 82 Week 6 Decision Trees Tutorial
  83. 83 Week 4 Tutorial 4 - Optimization
  84. 84 Week 3 Weka Tutorial
  85. 85 Week 2 Tutorial 2 - Linear Algebra (2)
  86. 86 Week 2 Tutorial 2 - Linear Algebra (1)
  87. 87 Week 1 Tutorial 1 - Probability Basics (2)
  88. 88 Week 1 Tutorial 1 - Probability Basics (1)

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