Machine Learning Pedro Domingos | 0DB676A6AAFF8C33F9749D5F9C0FA22BF336BC76

Machine Learning Pedro Domingos

Name	Machine Learning Pedro Domingos	DOWNLOAD Copy Link
Total Size	9.1 GB
Total Files	113
Last Seen	2024-11-02 23:48
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