Machine Learning Pedro Domingos

文件列表

• 01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4201.8 MB
• 01 Introduction & Inductive learning/2. What Is Machine Learning.mp447.3 MB
• 01 Introduction & Inductive learning/3. Applications of Machine Learning.mp472.6 MB
• 01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4138.4 MB
• 01 Introduction & Inductive learning/5. Types of Learning.mp469.7 MB
• 01 Introduction & Inductive learning/6. Machine Learning In Practice.mp487.6 MB
• 01 Introduction & Inductive learning/7. What Is Inductive Learning.mp428.1 MB
• 01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp459.3 MB
• 01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4182.5 MB
• 01 Introduction & Inductive learning/1. Class Information.mp427.9 MB
• 02 Decision Trees/1. Decision Trees.mp440.1 MB
• 02 Decision Trees/2. What Can a Decision Tree Represent.mp426.7 MB
• 02 Decision Trees/3. Growing a Decision Tree.mp427.8 MB
• 02 Decision Trees/4. Accuracy and Information Gain.mp4139.9 MB
• 02 Decision Trees/5. Learning with Non Boolean Features.mp440.8 MB
• 02 Decision Trees/6. The Parity Problem.mp432.0 MB
• 02 Decision Trees/7. Learning with Many Valued Attributes.mp439.4 MB
• 02 Decision Trees/8. Learning with Missing Values.mp472.0 MB
• 02 Decision Trees/9. The Overfitting Problem.mp449.1 MB
• 02 Decision Trees/10. Decision Tree Pruning.mp4132.2 MB
• 02 Decision Trees/11. Post Pruning Trees to Rules.mp4149.2 MB
• 02 Decision Trees/12. Scaling Up Decision Tree Learning.mp448.8 MB
• 03 Rule Induction/1. Rules vs. Decision Trees.mp4115.0 MB
• 03 Rule Induction/2. Learning a Set of Rules.mp494.7 MB
• 03 Rule Induction/3. Estimating Probabilities from Small Samples.mp476.0 MB
• 03 Rule Induction/4. Learning Rules for Multiple Classes.mp442.7 MB
• 03 Rule Induction/5. First Order Rules.mp476.8 MB
• 03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4186.9 MB
• 03 Rule Induction/7. Induction as Inverted Deduction.mp4132.9 MB
• 03 Rule Induction/8. Inverting Propositional Resolution.mp468.8 MB
• 03 Rule Induction/9. Inverting First Order Resolution.mp4149.1 MB
• 04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4151.1 MB
• 04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp498.1 MB
• 04 Instance-Based Learning/4. The Curse of Dimensionality.mp4128.3 MB
• 04 Instance-Based Learning/5. Feature Selection and Weighting.mp496.7 MB
• 04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp494.7 MB
• 04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp452.6 MB
• 04 Instance-Based Learning/8. Locally Weighted Regression.mp438.5 MB
• 04 Instance-Based Learning/9. Radial Basis Function Networks.mp431.6 MB
• 04 Instance-Based Learning/10 Case-Based Reasoning.mp437.0 MB
• 04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp426.4 MB
• 04 Instance-Based Learning/12. Collaborative Filtering.mp4148.8 MB
• 05 Bayesian Learning/1. Bayesian Methods.mp422.1 MB
• 05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4193.3 MB
• 05 Bayesian Learning/3. Basic Probability Formulas.mp446.8 MB
• 05 Bayesian Learning/4. MAP Learning.mp4101.4 MB
• 05 Bayesian Learning/5. Learning a Real-Valued Function.mp478.5 MB
• 05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp477.9 MB
• 05 Bayesian Learning/7. The Naive Bayes Classifier.mp4187.1 MB
• 05 Bayesian Learning/8. Text Classification.mp488.4 MB
• 05 Bayesian Learning/9. Bayesian Networks.mp4169.6 MB
• 05 Bayesian Learning/10. Inference in Bayesian Networks.mp432.3 MB
• 06 Neural Networks/1. Bayesian Network Review.mp418.5 MB
• 06 Neural Networks/2. Learning Bayesian Networks.mp431.2 MB
• 06 Neural Networks/3. The EM Algorithm.mp462.2 MB
• 06 Neural Networks/4. Example of EM.mp464.6 MB
• 06 Neural Networks/5. Learning Bayesian Network Structure.mp4140.1 MB
• 06 Neural Networks/6. The Structural EM Algorithm.mp419.9 MB
• 06 Neural Networks/7. Reverse Engineering the Brain.mp459.0 MB
• 06 Neural Networks/8. Neural Network Driving a Car.mp4108.5 MB
• 06 Neural Networks/9. How Neurons Work.mp463.0 MB
• 06 Neural Networks/10. The Perceptron.mp493.5 MB
• 06 Neural Networks/11. Perceptron Training.mp479.8 MB
• 06 Neural Networks/12. Gradient Descent.mp442.0 MB
• 07 Model Ensembles/1. Gradient Descent Continued.mp444.0 MB
• 07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp454.0 MB
• 07 Model Ensembles/3. Stochastic Gradient Descent.mp432.2 MB
• 07 Model Ensembles/4. Multilayer Perceptrons.mp472.3 MB
• 07 Model Ensembles/5. Backpropagation.mp495.8 MB
• 07 Model Ensembles/6. Issues in Backpropagation.mp4120.9 MB
• 07 Model Ensembles/7. Learning Hidden Layer Representations.mp468.0 MB
• 07 Model Ensembles/8. Expressiveness of Neural Networks.mp436.2 MB
• 07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp448.9 MB
• 07 Model Ensembles/10. Model Ensembles.mp414.8 MB
• 07 Model Ensembles/11. Bagging.mp443.4 MB
• 07 Model Ensembles/12. Boosting- The Basics.mp438.9 MB
• 08 Learning Theory/1. Boosting- The Details.mp459.0 MB
• 08 Learning Theory/2. Error Correcting Output Coding.mp484.8 MB
• 08 Learning Theory/3. Stacking.mp484.0 MB
• 08 Learning Theory/4. Learning Theory.mp413.7 MB
• 08 Learning Theory/5. 'No Free Lunch' Theorems.mp485.5 MB
• 08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp446.1 MB
• 08 Learning Theory/7. Bias and Variance.mp488.1 MB
• 08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp430.3 MB
• 08 Learning Theory/9. General Bias Variance Decomposition.mp484.1 MB
• 08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp430.9 MB
• 08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp431.0 MB
• 08 Learning Theory/12. PAC Learning.mp447.9 MB
• 08 Learning Theory/13. How Many Examples Are Enough.mp4108.7 MB
• 08 Learning Theory/14. Examples and Definition of PAC Learning.mp437.9 MB
• 09 Support Vector Machine/1. Agnostic Learning.mp498.0 MB
• 09 Support Vector Machine/2. VC Dimension.mp473.0 MB
• 09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp475.2 MB
• 09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp49.3 MB
• 09 Support Vector Machine/5. Support Vector Machines.mp455.3 MB
• 09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp498.8 MB
• 09 Support Vector Machine/7. Kernels.mp4124.0 MB
• 09 Support Vector Machine/8. Learning SVMs.mp4117.6 MB
• 09 Support Vector Machine/9. Constrained Optimization.mp4140.8 MB
• 09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4113.9 MB
• 09 Support Vector Machine/11. The SMO Algorithm.mp447.9 MB
• 10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp462.6 MB
• 10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp471.0 MB
• 10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp461.9 MB
• 10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp453.3 MB
• 10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4111.6 MB
• 10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp441.6 MB
• 10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp496.1 MB
• 10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp457.6 MB
• 10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp436.6 MB
• 10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4107.1 MB
• 10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp455.9 MB
• 10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp496.8 MB