种子简介

种子名称： Machine Learning Pedro Domingos
文件类型： 视频
文件数目： 113个文件
文件大小： 8.44 GB
收录时间： 2019-1-6 12:45
已经下载： 3次
资源热度： 129
最近下载： 2024-9-8 08:10

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:0db676a6aaff8c33f9749d5f9c0fa22bf336bc76&dn=Machine Learning Pedro Domingos 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

Machine Learning Pedro Domingos.torrent

01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4201.81MB

01 Introduction & Inductive learning/2. What Is Machine Learning.mp447.34MB

01 Introduction & Inductive learning/3. Applications of Machine Learning.mp472.6MB

01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4138.36MB

01 Introduction & Inductive learning/5. Types of Learning.mp469.72MB

01 Introduction & Inductive learning/6. Machine Learning In Practice.mp487.65MB

01 Introduction & Inductive learning/7. What Is Inductive Learning.mp428.07MB

01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp459.29MB

01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4182.51MB

01 Introduction & Inductive learning/1. Class Information.mp427.87MB

02 Decision Trees/1. Decision Trees.mp440.09MB

02 Decision Trees/2. What Can a Decision Tree Represent.mp426.71MB

02 Decision Trees/3. Growing a Decision Tree.mp427.79MB

02 Decision Trees/4. Accuracy and Information Gain.mp4139.93MB

02 Decision Trees/5. Learning with Non Boolean Features.mp440.83MB

02 Decision Trees/6. The Parity Problem.mp431.96MB

02 Decision Trees/7. Learning with Many Valued Attributes.mp439.4MB

02 Decision Trees/8. Learning with Missing Values.mp471.97MB

02 Decision Trees/9. The Overfitting Problem.mp449.15MB

02 Decision Trees/10. Decision Tree Pruning.mp4132.24MB

02 Decision Trees/11. Post Pruning Trees to Rules.mp4149.22MB

02 Decision Trees/12. Scaling Up Decision Tree Learning.mp448.81MB

03 Rule Induction/1. Rules vs. Decision Trees.mp4114.98MB

03 Rule Induction/2. Learning a Set of Rules.mp494.67MB

03 Rule Induction/3. Estimating Probabilities from Small Samples.mp475.97MB

03 Rule Induction/4. Learning Rules for Multiple Classes.mp442.73MB

03 Rule Induction/5. First Order Rules.mp476.76MB

03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4186.93MB

03 Rule Induction/7. Induction as Inverted Deduction.mp4132.9MB

03 Rule Induction/8. Inverting Propositional Resolution.mp468.84MB

03 Rule Induction/9. Inverting First Order Resolution.mp4149.08MB

04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4151.1MB

04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp498.11MB

04 Instance-Based Learning/4. The Curse of Dimensionality.mp4128.31MB

04 Instance-Based Learning/5. Feature Selection and Weighting.mp496.68MB

04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp494.67MB

04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp452.61MB

04 Instance-Based Learning/8. Locally Weighted Regression.mp438.54MB

04 Instance-Based Learning/9. Radial Basis Function Networks.mp431.65MB

04 Instance-Based Learning/10 Case-Based Reasoning.mp437.04MB

04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp426.37MB

04 Instance-Based Learning/12. Collaborative Filtering.mp4148.81MB

05 Bayesian Learning/1. Bayesian Methods.mp422.13MB

05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4193.26MB

05 Bayesian Learning/3. Basic Probability Formulas.mp446.79MB

05 Bayesian Learning/4. MAP Learning.mp4101.36MB

05 Bayesian Learning/5. Learning a Real-Valued Function.mp478.49MB

05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp477.89MB

05 Bayesian Learning/7. The Naive Bayes Classifier.mp4187.05MB

05 Bayesian Learning/8. Text Classification.mp488.41MB

05 Bayesian Learning/9. Bayesian Networks.mp4169.65MB

05 Bayesian Learning/10. Inference in Bayesian Networks.mp432.3MB

06 Neural Networks/1. Bayesian Network Review.mp418.45MB

06 Neural Networks/2. Learning Bayesian Networks.mp431.16MB

06 Neural Networks/3. The EM Algorithm.mp462.22MB

06 Neural Networks/4. Example of EM.mp464.65MB

06 Neural Networks/5. Learning Bayesian Network Structure.mp4140.09MB

06 Neural Networks/6. The Structural EM Algorithm.mp419.88MB

06 Neural Networks/7. Reverse Engineering the Brain.mp459MB

06 Neural Networks/8. Neural Network Driving a Car.mp4108.47MB

06 Neural Networks/9. How Neurons Work.mp462.95MB

06 Neural Networks/10. The Perceptron.mp493.5MB

06 Neural Networks/11. Perceptron Training.mp479.83MB

06 Neural Networks/12. Gradient Descent.mp442.02MB

07 Model Ensembles/1. Gradient Descent Continued.mp444.04MB

07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp453.96MB

07 Model Ensembles/3. Stochastic Gradient Descent.mp432.22MB

07 Model Ensembles/4. Multilayer Perceptrons.mp472.33MB

07 Model Ensembles/5. Backpropagation.mp495.82MB

07 Model Ensembles/6. Issues in Backpropagation.mp4120.86MB

07 Model Ensembles/7. Learning Hidden Layer Representations.mp467.97MB

07 Model Ensembles/8. Expressiveness of Neural Networks.mp436.22MB

07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp448.94MB

07 Model Ensembles/10. Model Ensembles.mp414.75MB

07 Model Ensembles/11. Bagging.mp443.39MB

07 Model Ensembles/12. Boosting- The Basics.mp438.93MB

08 Learning Theory/1. Boosting- The Details.mp459.03MB

08 Learning Theory/2. Error Correcting Output Coding.mp484.78MB

08 Learning Theory/3. Stacking.mp483.95MB

08 Learning Theory/4. Learning Theory.mp413.68MB

08 Learning Theory/5. 'No Free Lunch' Theorems.mp485.54MB

08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp446.05MB

08 Learning Theory/7. Bias and Variance.mp488.09MB

08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp430.26MB

08 Learning Theory/9. General Bias Variance Decomposition.mp484.14MB

08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp430.88MB

08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp431.01MB

08 Learning Theory/12. PAC Learning.mp447.87MB

08 Learning Theory/13. How Many Examples Are Enough.mp4108.75MB

08 Learning Theory/14. Examples and Definition of PAC Learning.mp437.93MB

09 Support Vector Machine/1. Agnostic Learning.mp497.96MB

09 Support Vector Machine/2. VC Dimension.mp472.96MB

09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp475.24MB

09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp49.29MB

09 Support Vector Machine/5. Support Vector Machines.mp455.28MB

09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp498.82MB

09 Support Vector Machine/7. Kernels.mp4123.96MB

09 Support Vector Machine/8. Learning SVMs.mp4117.58MB

09 Support Vector Machine/9. Constrained Optimization.mp4140.76MB

09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4113.9MB

09 Support Vector Machine/11. The SMO Algorithm.mp447.88MB

10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp462.58MB

10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp471.01MB

10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp461.91MB

10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp453.29MB

10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4111.61MB

10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp441.64MB

10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp496.14MB

10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp457.56MB

10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp436.59MB

10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4107.06MB

10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp455.93MB

10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp496.75MB