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Machine Learning Pedro Domingos

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Machine Learning Pedro Domingos

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Total Size

9.1 GB

Total Files

113

Last Seen

2024-12-23 23:26

Hash

0DB676A6AAFF8C33F9749D5F9C0FA22BF336BC76

/01 Introduction & Inductive learning/

10. A Framework for Studying Inductive Learning.mp4

211.6 MB

2. What Is Machine Learning.mp4

49.6 MB

3. Applications of Machine Learning.mp4

76.1 MB

4. Key Elements of Machine Learning.mp4

145.1 MB

5. Types of Learning.mp4

73.1 MB

6. Machine Learning In Practice.mp4

91.9 MB

7. What Is Inductive Learning.mp4

29.4 MB

8. When Should You Use Inductive Learning.mp4

62.2 MB

9. The Essence of Inductive Learning.mp4

191.4 MB

1. Class Information.mp4

29.2 MB

/02 Decision Trees/

1. Decision Trees.mp4

42.0 MB

2. What Can a Decision Tree Represent.mp4

28.0 MB

3. Growing a Decision Tree.mp4

29.1 MB

4. Accuracy and Information Gain.mp4

146.7 MB

5. Learning with Non Boolean Features.mp4

42.8 MB

6. The Parity Problem.mp4

33.5 MB

7. Learning with Many Valued Attributes.mp4

41.3 MB

8. Learning with Missing Values.mp4

75.5 MB

9. The Overfitting Problem.mp4

51.5 MB

10. Decision Tree Pruning.mp4

138.7 MB

11. Post Pruning Trees to Rules.mp4

156.5 MB

12. Scaling Up Decision Tree Learning.mp4

51.2 MB

/03 Rule Induction/

1. Rules vs. Decision Trees.mp4

120.6 MB

2. Learning a Set of Rules.mp4

99.3 MB

3. Estimating Probabilities from Small Samples.mp4

79.7 MB

4. Learning Rules for Multiple Classes.mp4

44.8 MB

5. First Order Rules.mp4

80.5 MB

6. Learning First Order Rules Using FOIL.mp4

196.0 MB

7. Induction as Inverted Deduction.mp4

139.4 MB

8. Inverting Propositional Resolution.mp4

72.2 MB

9. Inverting First Order Resolution.mp4

156.3 MB

/04 Instance-Based Learning/

1. The K-Nearest Neighbor Algorithm.mp4

158.4 MB

2. Theoretical Guarantees on k-NN.mp4

102.9 MB

4. The Curse of Dimensionality.mp4

134.5 MB

5. Feature Selection and Weighting.mp4

101.4 MB

6. Reducing the Computational Cost of k-NN.mp4

99.3 MB

7. Avoiding Overfitting in k-NN.mp4

55.2 MB

8. Locally Weighted Regression.mp4

40.4 MB

9. Radial Basis Function Networks.mp4

33.2 MB

10 Case-Based Reasoning.mp4

38.8 MB

11. Lazy vs. Eager Learning.mp4

27.6 MB

12. Collaborative Filtering.mp4

156.0 MB

/05 Bayesian Learning/

1. Bayesian Methods.mp4

23.2 MB

2. Bayes' Theorem and MAP Hypotheses.mp4

202.6 MB

3. Basic Probability Formulas.mp4

49.1 MB

4. MAP Learning.mp4

106.3 MB

5. Learning a Real-Valued Function.mp4

82.3 MB

6. Bayes Optimal Classifier and Gibbs Classifier.mp4

81.7 MB

7. The Naive Bayes Classifier.mp4

196.1 MB

8. Text Classification.mp4

92.7 MB

9. Bayesian Networks.mp4

177.9 MB

10. Inference in Bayesian Networks.mp4

33.9 MB

/06 Neural Networks/

1. Bayesian Network Review.mp4

19.3 MB

2. Learning Bayesian Networks.mp4

32.7 MB

3. The EM Algorithm.mp4

65.2 MB

4. Example of EM.mp4

67.8 MB

5. Learning Bayesian Network Structure.mp4

146.9 MB

6. The Structural EM Algorithm.mp4

20.8 MB

7. Reverse Engineering the Brain.mp4

61.9 MB

8. Neural Network Driving a Car.mp4

113.7 MB

9. How Neurons Work.mp4

66.0 MB

10. The Perceptron.mp4

98.0 MB

11. Perceptron Training.mp4

83.7 MB

12. Gradient Descent.mp4

44.1 MB

/07 Model Ensembles/

1. Gradient Descent Continued.mp4

46.2 MB

2. Gradient Descent vs Perceptron Training.mp4

56.6 MB

3. Stochastic Gradient Descent.mp4

33.8 MB

4. Multilayer Perceptrons.mp4

75.8 MB

5. Backpropagation.mp4

100.5 MB

6. Issues in Backpropagation.mp4

126.7 MB

7. Learning Hidden Layer Representations.mp4

71.3 MB

8. Expressiveness of Neural Networks.mp4

38.0 MB

9. Avoiding Overfitting in Neural Networks.mp4

51.3 MB

10. Model Ensembles.mp4

15.5 MB

11. Bagging.mp4

45.5 MB

12. Boosting- The Basics.mp4

40.8 MB

/08 Learning Theory/

1. Boosting- The Details.mp4

61.9 MB

2. Error Correcting Output Coding.mp4

88.9 MB

3. Stacking.mp4

88.0 MB

4. Learning Theory.mp4

14.3 MB

5. 'No Free Lunch' Theorems.mp4

89.7 MB

6. Practical Consequences of 'No Free Lunch'.mp4

48.3 MB

7. Bias and Variance.mp4

92.4 MB

8. Bias Variance Decomposition for Squared Loss.mp4

31.7 MB

9. General Bias Variance Decomposition.mp4

88.2 MB

10. Bias-Variance Decomposition for Zer -One Loss.mp4

32.4 MB

11. Bias and Variance for Other Loss Functions.mp4

32.5 MB

12. PAC Learning.mp4

50.2 MB

13. How Many Examples Are Enough.mp4

114.0 MB

14. Examples and Definition of PAC Learning.mp4

39.8 MB

/09 Support Vector Machine/

1. Agnostic Learning.mp4

102.7 MB

2. VC Dimension.mp4

76.5 MB

3. VC Dimension of Hyperplanes.mp4

78.9 MB

4. Sample Complexity from VC Dimension.mp4

9.7 MB

5. Support Vector Machines.mp4

58.0 MB

6. Perceptrons as Instance-Based Learning.mp4

103.6 MB

7. Kernels.mp4

130.0 MB

8. Learning SVMs.mp4

123.3 MB

9. Constrained Optimization.mp4

147.6 MB

10. Optimization with Inequality Constraints.mp4

119.4 MB

11. The SMO Algorithm.mp4

50.2 MB

/10 Clustering and Dimensionality Reduction/

1. Handling Noisy Data in SVMs.mp4

65.6 MB

2. Generalization Bounds for SVMs.mp4

74.5 MB

3. Clustering and Dimensionality Reduction.mp4

64.9 MB

4. K-Means Clustering.mp4

55.9 MB

5. Mixture Models.mp4

117.0 MB

6. Mixtures of Gaussians.mp4

43.7 MB

7. EM Algorithm for Mixtures of Gaussians.mp4

100.8 MB

8. Mixture Models vs K-Means vs. Bayesian Networks.mp4

60.4 MB

9. Hierarchical Clustering.mp4

38.4 MB

10. Principal Components Analysis.mp4

112.3 MB

11. Multidimensional Scaling.mp4

58.6 MB

12. Nonlinear Dimensionality Reduction.mp4

101.5 MB

 

Total files 113


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