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Machine Learning Pedro Domingos
magnet:?xt=urn:btih:0db676a6aaff8c33f9749d5f9c0fa22bf336bc76&dn=Machine Learning Pedro Domingos
磁力链接详情
Hash值:
0db676a6aaff8c33f9749d5f9c0fa22bf336bc76
点击数:
117
文件大小:
8.44 GB
文件数量:
113
创建日期:
2019-1-6 12:45
最后访问:
2024-5-23 05:07
访问标签:
Machine
Learning
Pedro
Domingos
文件列表详情
01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4 201.81 MB
01 Introduction & Inductive learning/2. What Is Machine Learning.mp4 47.34 MB
01 Introduction & Inductive learning/3. Applications of Machine Learning.mp4 72.6 MB
01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4 138.36 MB
01 Introduction & Inductive learning/5. Types of Learning.mp4 69.72 MB
01 Introduction & Inductive learning/6. Machine Learning In Practice.mp4 87.65 MB
01 Introduction & Inductive learning/7. What Is Inductive Learning.mp4 28.07 MB
01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp4 59.29 MB
01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4 182.51 MB
01 Introduction & Inductive learning/1. Class Information.mp4 27.87 MB
02 Decision Trees/1. Decision Trees.mp4 40.09 MB
02 Decision Trees/2. What Can a Decision Tree Represent.mp4 26.71 MB
02 Decision Trees/3. Growing a Decision Tree.mp4 27.79 MB
02 Decision Trees/4. Accuracy and Information Gain.mp4 139.93 MB
02 Decision Trees/5. Learning with Non Boolean Features.mp4 40.83 MB
02 Decision Trees/6. The Parity Problem.mp4 31.96 MB
02 Decision Trees/7. Learning with Many Valued Attributes.mp4 39.4 MB
02 Decision Trees/8. Learning with Missing Values.mp4 71.97 MB
02 Decision Trees/9. The Overfitting Problem.mp4 49.15 MB
02 Decision Trees/10. Decision Tree Pruning.mp4 132.24 MB
02 Decision Trees/11. Post Pruning Trees to Rules.mp4 149.22 MB
02 Decision Trees/12. Scaling Up Decision Tree Learning.mp4 48.81 MB
03 Rule Induction/1. Rules vs. Decision Trees.mp4 114.98 MB
03 Rule Induction/2. Learning a Set of Rules.mp4 94.67 MB
03 Rule Induction/3. Estimating Probabilities from Small Samples.mp4 75.97 MB
03 Rule Induction/4. Learning Rules for Multiple Classes.mp4 42.73 MB
03 Rule Induction/5. First Order Rules.mp4 76.76 MB
03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4 186.93 MB
03 Rule Induction/7. Induction as Inverted Deduction.mp4 132.9 MB
03 Rule Induction/8. Inverting Propositional Resolution.mp4 68.84 MB
03 Rule Induction/9. Inverting First Order Resolution.mp4 149.08 MB
04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4 151.1 MB
04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4 98.11 MB
04 Instance-Based Learning/4. The Curse of Dimensionality.mp4 128.31 MB
04 Instance-Based Learning/5. Feature Selection and Weighting.mp4 96.68 MB
04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp4 94.67 MB
04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp4 52.61 MB
04 Instance-Based Learning/8. Locally Weighted Regression.mp4 38.54 MB
04 Instance-Based Learning/9. Radial Basis Function Networks.mp4 31.65 MB
04 Instance-Based Learning/10 Case-Based Reasoning.mp4 37.04 MB
04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp4 26.37 MB
04 Instance-Based Learning/12. Collaborative Filtering.mp4 148.81 MB
05 Bayesian Learning/1. Bayesian Methods.mp4 22.13 MB
05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4 193.26 MB
05 Bayesian Learning/3. Basic Probability Formulas.mp4 46.79 MB
05 Bayesian Learning/4. MAP Learning.mp4 101.36 MB
05 Bayesian Learning/5. Learning a Real-Valued Function.mp4 78.49 MB
05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp4 77.89 MB
05 Bayesian Learning/7. The Naive Bayes Classifier.mp4 187.05 MB
05 Bayesian Learning/8. Text Classification.mp4 88.41 MB
05 Bayesian Learning/9. Bayesian Networks.mp4 169.65 MB
05 Bayesian Learning/10. Inference in Bayesian Networks.mp4 32.3 MB
06 Neural Networks/1. Bayesian Network Review.mp4 18.45 MB
06 Neural Networks/2. Learning Bayesian Networks.mp4 31.16 MB
06 Neural Networks/3. The EM Algorithm.mp4 62.22 MB
06 Neural Networks/4. Example of EM.mp4 64.65 MB
06 Neural Networks/5. Learning Bayesian Network Structure.mp4 140.09 MB
06 Neural Networks/6. The Structural EM Algorithm.mp4 19.88 MB
06 Neural Networks/7. Reverse Engineering the Brain.mp4 59 MB
06 Neural Networks/8. Neural Network Driving a Car.mp4 108.47 MB
06 Neural Networks/9. How Neurons Work.mp4 62.95 MB
06 Neural Networks/10. The Perceptron.mp4 93.5 MB
06 Neural Networks/11. Perceptron Training.mp4 79.83 MB
06 Neural Networks/12. Gradient Descent.mp4 42.02 MB
07 Model Ensembles/1. Gradient Descent Continued.mp4 44.04 MB
07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4 53.96 MB
07 Model Ensembles/3. Stochastic Gradient Descent.mp4 32.22 MB
07 Model Ensembles/4. Multilayer Perceptrons.mp4 72.33 MB
07 Model Ensembles/5. Backpropagation.mp4 95.82 MB
07 Model Ensembles/6. Issues in Backpropagation.mp4 120.86 MB
07 Model Ensembles/7. Learning Hidden Layer Representations.mp4 67.97 MB
07 Model Ensembles/8. Expressiveness of Neural Networks.mp4 36.22 MB
07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4 48.94 MB
07 Model Ensembles/10. Model Ensembles.mp4 14.75 MB
07 Model Ensembles/11. Bagging.mp4 43.39 MB
07 Model Ensembles/12. Boosting- The Basics.mp4 38.93 MB
08 Learning Theory/1. Boosting- The Details.mp4 59.03 MB
08 Learning Theory/2. Error Correcting Output Coding.mp4 84.78 MB
08 Learning Theory/3. Stacking.mp4 83.95 MB
08 Learning Theory/4. Learning Theory.mp4 13.68 MB
08 Learning Theory/5. 'No Free Lunch' Theorems.mp4 85.54 MB
08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4 46.05 MB
08 Learning Theory/7. Bias and Variance.mp4 88.09 MB
08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4 30.26 MB
08 Learning Theory/9. General Bias Variance Decomposition.mp4 84.14 MB
08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4 30.88 MB
08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4 31.01 MB
08 Learning Theory/12. PAC Learning.mp4 47.87 MB
08 Learning Theory/13. How Many Examples Are Enough.mp4 108.75 MB
08 Learning Theory/14. Examples and Definition of PAC Learning.mp4 37.93 MB
09 Support Vector Machine/1. Agnostic Learning.mp4 97.96 MB
09 Support Vector Machine/2. VC Dimension.mp4 72.96 MB
09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp4 75.24 MB
09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4 9.29 MB
09 Support Vector Machine/5. Support Vector Machines.mp4 55.28 MB
09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4 98.82 MB
09 Support Vector Machine/7. Kernels.mp4 123.96 MB
09 Support Vector Machine/8. Learning SVMs.mp4 117.58 MB
09 Support Vector Machine/9. Constrained Optimization.mp4 140.76 MB
09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4 113.9 MB
09 Support Vector Machine/11. The SMO Algorithm.mp4 47.88 MB
10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp4 62.58 MB
10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp4 71.01 MB
10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp4 61.91 MB
10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp4 53.29 MB
10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4 111.61 MB
10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp4 41.64 MB
10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4 96.14 MB
10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp4 57.56 MB
10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp4 36.59 MB
10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4 107.06 MB
10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp4 55.93 MB
10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4 96.75 MB
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