Machine Learning Pedro Domingos

1. 01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4201.81MB
2. 01 Introduction & Inductive learning/2. What Is Machine Learning.mp447.34MB
3. 01 Introduction & Inductive learning/3. Applications of Machine Learning.mp472.6MB
4. 01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4138.36MB
5. 01 Introduction & Inductive learning/5. Types of Learning.mp469.72MB
6. 01 Introduction & Inductive learning/6. Machine Learning In Practice.mp487.65MB
7. 01 Introduction & Inductive learning/7. What Is Inductive Learning.mp428.07MB
8. 01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp459.29MB
9. 01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4182.51MB
10. 01 Introduction & Inductive learning/1. Class Information.mp427.87MB
11. 02 Decision Trees/1. Decision Trees.mp440.09MB
12. 02 Decision Trees/2. What Can a Decision Tree Represent.mp426.71MB
13. 02 Decision Trees/3. Growing a Decision Tree.mp427.79MB
14. 02 Decision Trees/4. Accuracy and Information Gain.mp4139.93MB
15. 02 Decision Trees/5. Learning with Non Boolean Features.mp440.83MB
16. 02 Decision Trees/6. The Parity Problem.mp431.96MB
17. 02 Decision Trees/7. Learning with Many Valued Attributes.mp439.4MB
18. 02 Decision Trees/8. Learning with Missing Values.mp471.97MB
19. 02 Decision Trees/9. The Overfitting Problem.mp449.15MB
20. 02 Decision Trees/10. Decision Tree Pruning.mp4132.24MB
21. 02 Decision Trees/11. Post Pruning Trees to Rules.mp4149.22MB
22. 02 Decision Trees/12. Scaling Up Decision Tree Learning.mp448.81MB
23. 03 Rule Induction/1. Rules vs. Decision Trees.mp4114.98MB
24. 03 Rule Induction/2. Learning a Set of Rules.mp494.67MB
25. 03 Rule Induction/3. Estimating Probabilities from Small Samples.mp475.97MB
26. 03 Rule Induction/4. Learning Rules for Multiple Classes.mp442.73MB
27. 03 Rule Induction/5. First Order Rules.mp476.76MB
28. 03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4186.93MB
29. 03 Rule Induction/7. Induction as Inverted Deduction.mp4132.9MB
30. 03 Rule Induction/8. Inverting Propositional Resolution.mp468.84MB
31. 03 Rule Induction/9. Inverting First Order Resolution.mp4149.08MB
32. 04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4151.1MB
33. 04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp498.11MB
34. 04 Instance-Based Learning/4. The Curse of Dimensionality.mp4128.31MB
35. 04 Instance-Based Learning/5. Feature Selection and Weighting.mp496.68MB
36. 04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp494.67MB
37. 04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp452.61MB
38. 04 Instance-Based Learning/8. Locally Weighted Regression.mp438.54MB
39. 04 Instance-Based Learning/9. Radial Basis Function Networks.mp431.65MB
40. 04 Instance-Based Learning/10 Case-Based Reasoning.mp437.04MB
41. 04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp426.37MB
42. 04 Instance-Based Learning/12. Collaborative Filtering.mp4148.81MB
43. 05 Bayesian Learning/1. Bayesian Methods.mp422.13MB
44. 05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4193.26MB
45. 05 Bayesian Learning/3. Basic Probability Formulas.mp446.79MB
46. 05 Bayesian Learning/4. MAP Learning.mp4101.36MB
47. 05 Bayesian Learning/5. Learning a Real-Valued Function.mp478.49MB
48. 05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp477.89MB
49. 05 Bayesian Learning/7. The Naive Bayes Classifier.mp4187.05MB
50. 05 Bayesian Learning/8. Text Classification.mp488.41MB
51. 05 Bayesian Learning/9. Bayesian Networks.mp4169.65MB
52. 05 Bayesian Learning/10. Inference in Bayesian Networks.mp432.3MB
53. 06 Neural Networks/1. Bayesian Network Review.mp418.45MB
54. 06 Neural Networks/2. Learning Bayesian Networks.mp431.16MB
55. 06 Neural Networks/3. The EM Algorithm.mp462.22MB
56. 06 Neural Networks/4. Example of EM.mp464.65MB
57. 06 Neural Networks/5. Learning Bayesian Network Structure.mp4140.09MB
58. 06 Neural Networks/6. The Structural EM Algorithm.mp419.88MB
59. 06 Neural Networks/7. Reverse Engineering the Brain.mp459MB
60. 06 Neural Networks/8. Neural Network Driving a Car.mp4108.47MB
61. 06 Neural Networks/9. How Neurons Work.mp462.95MB
62. 06 Neural Networks/10. The Perceptron.mp493.5MB
63. 06 Neural Networks/11. Perceptron Training.mp479.83MB
64. 06 Neural Networks/12. Gradient Descent.mp442.02MB
65. 07 Model Ensembles/1. Gradient Descent Continued.mp444.04MB
66. 07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp453.96MB
67. 07 Model Ensembles/3. Stochastic Gradient Descent.mp432.22MB
68. 07 Model Ensembles/4. Multilayer Perceptrons.mp472.33MB
69. 07 Model Ensembles/5. Backpropagation.mp495.82MB
70. 07 Model Ensembles/6. Issues in Backpropagation.mp4120.86MB
71. 07 Model Ensembles/7. Learning Hidden Layer Representations.mp467.97MB
72. 07 Model Ensembles/8. Expressiveness of Neural Networks.mp436.22MB
73. 07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp448.94MB
74. 07 Model Ensembles/10. Model Ensembles.mp414.75MB
75. 07 Model Ensembles/11. Bagging.mp443.39MB
76. 07 Model Ensembles/12. Boosting- The Basics.mp438.93MB
77. 08 Learning Theory/1. Boosting- The Details.mp459.03MB
78. 08 Learning Theory/2. Error Correcting Output Coding.mp484.78MB
79. 08 Learning Theory/3. Stacking.mp483.95MB
80. 08 Learning Theory/4. Learning Theory.mp413.68MB
81. 08 Learning Theory/5. 'No Free Lunch' Theorems.mp485.54MB
82. 08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp446.05MB
83. 08 Learning Theory/7. Bias and Variance.mp488.09MB
84. 08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp430.26MB
85. 08 Learning Theory/9. General Bias Variance Decomposition.mp484.14MB
86. 08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp430.88MB
87. 08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp431.01MB
88. 08 Learning Theory/12. PAC Learning.mp447.87MB
89. 08 Learning Theory/13. How Many Examples Are Enough.mp4108.75MB
90. 08 Learning Theory/14. Examples and Definition of PAC Learning.mp437.93MB
91. 09 Support Vector Machine/1. Agnostic Learning.mp497.96MB
92. 09 Support Vector Machine/2. VC Dimension.mp472.96MB
93. 09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp475.24MB
94. 09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp49.29MB
95. 09 Support Vector Machine/5. Support Vector Machines.mp455.28MB
96. 09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp498.82MB
97. 09 Support Vector Machine/7. Kernels.mp4123.96MB
98. 09 Support Vector Machine/8. Learning SVMs.mp4117.58MB
99. 09 Support Vector Machine/9. Constrained Optimization.mp4140.76MB
100. 09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4113.9MB
101. 09 Support Vector Machine/11. The SMO Algorithm.mp447.88MB
102. 10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp462.58MB
103. 10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp471.01MB
104. 10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp461.91MB
105. 10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp453.29MB
106. 10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4111.61MB
107. 10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp441.64MB
108. 10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp496.14MB
109. 10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp457.56MB
110. 10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp436.59MB
111. 10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4107.06MB
112. 10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp455.93MB
113. 10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp496.75MB