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Machine Learning Pedro Domingos
磁力链接/BT种子名称
Machine Learning Pedro Domingos
磁力链接/BT种子简介
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0db676a6aaff8c33f9749d5f9c0fa22bf336bc76
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文件列表
01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4
211.6 MB
01 Introduction & Inductive learning/2. What Is Machine Learning.mp4
49.6 MB
01 Introduction & Inductive learning/3. Applications of Machine Learning.mp4
76.1 MB
01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4
145.1 MB
01 Introduction & Inductive learning/5. Types of Learning.mp4
73.1 MB
01 Introduction & Inductive learning/6. Machine Learning In Practice.mp4
91.9 MB
01 Introduction & Inductive learning/7. What Is Inductive Learning.mp4
29.4 MB
01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp4
62.2 MB
01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4
191.4 MB
01 Introduction & Inductive learning/1. Class Information.mp4
29.2 MB
02 Decision Trees/1. Decision Trees.mp4
42.0 MB
02 Decision Trees/2. What Can a Decision Tree Represent.mp4
28.0 MB
02 Decision Trees/3. Growing a Decision Tree.mp4
29.1 MB
02 Decision Trees/4. Accuracy and Information Gain.mp4
146.7 MB
02 Decision Trees/5. Learning with Non Boolean Features.mp4
42.8 MB
02 Decision Trees/6. The Parity Problem.mp4
33.5 MB
02 Decision Trees/7. Learning with Many Valued Attributes.mp4
41.3 MB
02 Decision Trees/8. Learning with Missing Values.mp4
75.5 MB
02 Decision Trees/9. The Overfitting Problem.mp4
51.5 MB
02 Decision Trees/10. Decision Tree Pruning.mp4
138.7 MB
02 Decision Trees/11. Post Pruning Trees to Rules.mp4
156.5 MB
02 Decision Trees/12. Scaling Up Decision Tree Learning.mp4
51.2 MB
03 Rule Induction/1. Rules vs. Decision Trees.mp4
120.6 MB
03 Rule Induction/2. Learning a Set of Rules.mp4
99.3 MB
03 Rule Induction/3. Estimating Probabilities from Small Samples.mp4
79.7 MB
03 Rule Induction/4. Learning Rules for Multiple Classes.mp4
44.8 MB
03 Rule Induction/5. First Order Rules.mp4
80.5 MB
03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4
196.0 MB
03 Rule Induction/7. Induction as Inverted Deduction.mp4
139.4 MB
03 Rule Induction/8. Inverting Propositional Resolution.mp4
72.2 MB
03 Rule Induction/9. Inverting First Order Resolution.mp4
156.3 MB
04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4
158.4 MB
04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4
102.9 MB
04 Instance-Based Learning/4. The Curse of Dimensionality.mp4
134.5 MB
04 Instance-Based Learning/5. Feature Selection and Weighting.mp4
101.4 MB
04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp4
99.3 MB
04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp4
55.2 MB
04 Instance-Based Learning/8. Locally Weighted Regression.mp4
40.4 MB
04 Instance-Based Learning/9. Radial Basis Function Networks.mp4
33.2 MB
04 Instance-Based Learning/10 Case-Based Reasoning.mp4
38.8 MB
04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp4
27.6 MB
04 Instance-Based Learning/12. Collaborative Filtering.mp4
156.0 MB
05 Bayesian Learning/1. Bayesian Methods.mp4
23.2 MB
05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4
202.6 MB
05 Bayesian Learning/3. Basic Probability Formulas.mp4
49.1 MB
05 Bayesian Learning/4. MAP Learning.mp4
106.3 MB
05 Bayesian Learning/5. Learning a Real-Valued Function.mp4
82.3 MB
05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp4
81.7 MB
05 Bayesian Learning/7. The Naive Bayes Classifier.mp4
196.1 MB
05 Bayesian Learning/8. Text Classification.mp4
92.7 MB
05 Bayesian Learning/9. Bayesian Networks.mp4
177.9 MB
05 Bayesian Learning/10. Inference in Bayesian Networks.mp4
33.9 MB
06 Neural Networks/1. Bayesian Network Review.mp4
19.3 MB
06 Neural Networks/2. Learning Bayesian Networks.mp4
32.7 MB
06 Neural Networks/3. The EM Algorithm.mp4
65.2 MB
06 Neural Networks/4. Example of EM.mp4
67.8 MB
06 Neural Networks/5. Learning Bayesian Network Structure.mp4
146.9 MB
06 Neural Networks/6. The Structural EM Algorithm.mp4
20.8 MB
06 Neural Networks/7. Reverse Engineering the Brain.mp4
61.9 MB
06 Neural Networks/8. Neural Network Driving a Car.mp4
113.7 MB
06 Neural Networks/9. How Neurons Work.mp4
66.0 MB
06 Neural Networks/10. The Perceptron.mp4
98.0 MB
06 Neural Networks/11. Perceptron Training.mp4
83.7 MB
06 Neural Networks/12. Gradient Descent.mp4
44.1 MB
07 Model Ensembles/1. Gradient Descent Continued.mp4
46.2 MB
07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4
56.6 MB
07 Model Ensembles/3. Stochastic Gradient Descent.mp4
33.8 MB
07 Model Ensembles/4. Multilayer Perceptrons.mp4
75.8 MB
07 Model Ensembles/5. Backpropagation.mp4
100.5 MB
07 Model Ensembles/6. Issues in Backpropagation.mp4
126.7 MB
07 Model Ensembles/7. Learning Hidden Layer Representations.mp4
71.3 MB
07 Model Ensembles/8. Expressiveness of Neural Networks.mp4
38.0 MB
07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4
51.3 MB
07 Model Ensembles/10. Model Ensembles.mp4
15.5 MB
07 Model Ensembles/11. Bagging.mp4
45.5 MB
07 Model Ensembles/12. Boosting- The Basics.mp4
40.8 MB
08 Learning Theory/1. Boosting- The Details.mp4
61.9 MB
08 Learning Theory/2. Error Correcting Output Coding.mp4
88.9 MB
08 Learning Theory/3. Stacking.mp4
88.0 MB
08 Learning Theory/4. Learning Theory.mp4
14.3 MB
08 Learning Theory/5. 'No Free Lunch' Theorems.mp4
89.7 MB
08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4
48.3 MB
08 Learning Theory/7. Bias and Variance.mp4
92.4 MB
08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4
31.7 MB
08 Learning Theory/9. General Bias Variance Decomposition.mp4
88.2 MB
08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4
32.4 MB
08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4
32.5 MB
08 Learning Theory/12. PAC Learning.mp4
50.2 MB
08 Learning Theory/13. How Many Examples Are Enough.mp4
114.0 MB
08 Learning Theory/14. Examples and Definition of PAC Learning.mp4
39.8 MB
09 Support Vector Machine/1. Agnostic Learning.mp4
102.7 MB
09 Support Vector Machine/2. VC Dimension.mp4
76.5 MB
09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp4
78.9 MB
09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4
9.7 MB
09 Support Vector Machine/5. Support Vector Machines.mp4
58.0 MB
09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4
103.6 MB
09 Support Vector Machine/7. Kernels.mp4
130.0 MB
09 Support Vector Machine/8. Learning SVMs.mp4
123.3 MB
09 Support Vector Machine/9. Constrained Optimization.mp4
147.6 MB
09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4
119.4 MB
09 Support Vector Machine/11. The SMO Algorithm.mp4
50.2 MB
10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp4
65.6 MB
10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp4
74.5 MB
10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp4
64.9 MB
10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp4
55.9 MB
10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4
117.0 MB
10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp4
43.7 MB
10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4
100.8 MB
10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp4
60.4 MB
10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp4
38.4 MB
10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4
112.3 MB
10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp4
58.6 MB
10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4
101.5 MB
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