| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4 |
201.8 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/2. What Is Machine Learning.mp4 |
47.3 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/3. Applications of Machine Learning.mp4 |
72.6 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4 |
138.4 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/5. Types of Learning.mp4 |
69.7 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/6. Machine Learning In Practice.mp4 |
87.6 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/7. What Is Inductive Learning.mp4 |
28.1 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp4 |
59.3 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4 |
182.5 MB |
| Machine Learning Pedro Domingos/01 Introduction & Inductive learning/1. Class Information.mp4 |
27.9 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/1. Decision Trees.mp4 |
40.1 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/2. What Can a Decision Tree Represent.mp4 |
26.7 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/3. Growing a Decision Tree.mp4 |
27.8 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/4. Accuracy and Information Gain.mp4 |
139.9 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/5. Learning with Non Boolean Features.mp4 |
40.8 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/6. The Parity Problem.mp4 |
32 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/7. Learning with Many Valued Attributes.mp4 |
39.4 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/8. Learning with Missing Values.mp4 |
72 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/9. The Overfitting Problem.mp4 |
49.1 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/10. Decision Tree Pruning.mp4 |
132.2 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/11. Post Pruning Trees to Rules.mp4 |
149.2 MB |
| Machine Learning Pedro Domingos/02 Decision Trees/12. Scaling Up Decision Tree Learning.mp4 |
48.8 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/1. Rules vs. Decision Trees.mp4 |
115 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/2. Learning a Set of Rules.mp4 |
94.7 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/3. Estimating Probabilities from Small Samples.mp4 |
76 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/4. Learning Rules for Multiple Classes.mp4 |
42.7 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/5. First Order Rules.mp4 |
76.8 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4 |
186.9 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/7. Induction as Inverted Deduction.mp4 |
132.9 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/8. Inverting Propositional Resolution.mp4 |
68.8 MB |
| Machine Learning Pedro Domingos/03 Rule Induction/9. Inverting First Order Resolution.mp4 |
149.1 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4 |
151.1 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4 |
98.1 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/4. The Curse of Dimensionality.mp4 |
128.3 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/5. Feature Selection and Weighting.mp4 |
96.7 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp4 |
94.7 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp4 |
52.6 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/8. Locally Weighted Regression.mp4 |
38.5 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/9. Radial Basis Function Networks.mp4 |
31.6 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/10 Case-Based Reasoning.mp4 |
37 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp4 |
26.4 MB |
| Machine Learning Pedro Domingos/04 Instance-Based Learning/12. Collaborative Filtering.mp4 |
148.8 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/1. Bayesian Methods.mp4 |
22.1 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4 |
193.3 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/3. Basic Probability Formulas.mp4 |
46.8 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/4. MAP Learning.mp4 |
101.4 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/5. Learning a Real-Valued Function.mp4 |
78.5 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp4 |
77.9 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/7. The Naive Bayes Classifier.mp4 |
187.1 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/8. Text Classification.mp4 |
88.4 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/9. Bayesian Networks.mp4 |
169.6 MB |
| Machine Learning Pedro Domingos/05 Bayesian Learning/10. Inference in Bayesian Networks.mp4 |
32.3 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/1. Bayesian Network Review.mp4 |
18.5 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/2. Learning Bayesian Networks.mp4 |
31.2 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/3. The EM Algorithm.mp4 |
62.2 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/4. Example of EM.mp4 |
64.6 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/5. Learning Bayesian Network Structure.mp4 |
140.1 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/6. The Structural EM Algorithm.mp4 |
19.9 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/7. Reverse Engineering the Brain.mp4 |
59 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/8. Neural Network Driving a Car.mp4 |
108.5 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/9. How Neurons Work.mp4 |
63 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/10. The Perceptron.mp4 |
93.5 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/11. Perceptron Training.mp4 |
79.8 MB |
| Machine Learning Pedro Domingos/06 Neural Networks/12. Gradient Descent.mp4 |
42 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/1. Gradient Descent Continued.mp4 |
44 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4 |
54 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/3. Stochastic Gradient Descent.mp4 |
32.2 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/4. Multilayer Perceptrons.mp4 |
72.3 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/5. Backpropagation.mp4 |
95.8 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/6. Issues in Backpropagation.mp4 |
120.9 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/7. Learning Hidden Layer Representations.mp4 |
68 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/8. Expressiveness of Neural Networks.mp4 |
36.2 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4 |
48.9 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/10. Model Ensembles.mp4 |
14.8 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/11. Bagging.mp4 |
43.4 MB |
| Machine Learning Pedro Domingos/07 Model Ensembles/12. Boosting- The Basics.mp4 |
38.9 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/1. Boosting- The Details.mp4 |
59 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/2. Error Correcting Output Coding.mp4 |
84.8 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/3. Stacking.mp4 |
84 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/4. Learning Theory.mp4 |
13.7 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/5. 'No Free Lunch' Theorems.mp4 |
85.5 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4 |
46.1 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/7. Bias and Variance.mp4 |
88.1 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4 |
30.3 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/9. General Bias Variance Decomposition.mp4 |
84.1 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4 |
30.9 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4 |
31 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/12. PAC Learning.mp4 |
47.9 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/13. How Many Examples Are Enough.mp4 |
108.7 MB |
| Machine Learning Pedro Domingos/08 Learning Theory/14. Examples and Definition of PAC Learning.mp4 |
37.9 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/1. Agnostic Learning.mp4 |
98 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/2. VC Dimension.mp4 |
73 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp4 |
75.2 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4 |
9.3 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/5. Support Vector Machines.mp4 |
55.3 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4 |
98.8 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/7. Kernels.mp4 |
124 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/8. Learning SVMs.mp4 |
117.6 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/9. Constrained Optimization.mp4 |
140.8 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4 |
113.9 MB |
| Machine Learning Pedro Domingos/09 Support Vector Machine/11. The SMO Algorithm.mp4 |
47.9 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp4 |
62.6 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp4 |
71 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp4 |
61.9 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp4 |
53.3 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4 |
111.6 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp4 |
41.6 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4 |
96.1 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp4 |
57.6 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp4 |
36.6 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4 |
107.1 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp4 |
55.9 MB |
| Machine Learning Pedro Domingos/10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4 |
96.8 MB |
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