| Path | Size |
| 01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4 | 211.61MB |
| 01 Introduction & Inductive learning/2. What Is Machine Learning.mp4 | 49.64MB |
| 01 Introduction & Inductive learning/3. Applications of Machine Learning.mp4 | 76.12MB |
| 01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4 | 145.08MB |
| 01 Introduction & Inductive learning/5. Types of Learning.mp4 | 73.11MB |
| 01 Introduction & Inductive learning/6. Machine Learning In Practice.mp4 | 91.90MB |
| 01 Introduction & Inductive learning/7. What Is Inductive Learning.mp4 | 29.44MB |
| 01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp4 | 62.17MB |
| 01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4 | 191.37MB |
| 01 Introduction & Inductive learning/1. Class Information.mp4 | 29.22MB |
| 02 Decision Trees/1. Decision Trees.mp4 | 42.04MB |
| 02 Decision Trees/2. What Can a Decision Tree Represent.mp4 | 28.01MB |
| 02 Decision Trees/3. Growing a Decision Tree.mp4 | 29.14MB |
| 02 Decision Trees/4. Accuracy and Information Gain.mp4 | 146.73MB |
| 02 Decision Trees/5. Learning with Non Boolean Features.mp4 | 42.81MB |
| 02 Decision Trees/6. The Parity Problem.mp4 | 33.52MB |
| 02 Decision Trees/7. Learning with Many Valued Attributes.mp4 | 41.32MB |
| 02 Decision Trees/8. Learning with Missing Values.mp4 | 75.46MB |
| 02 Decision Trees/9. The Overfitting Problem.mp4 | 51.53MB |
| 02 Decision Trees/10. Decision Tree Pruning.mp4 | 138.66MB |
| 02 Decision Trees/11. Post Pruning Trees to Rules.mp4 | 156.47MB |
| 02 Decision Trees/12. Scaling Up Decision Tree Learning.mp4 | 51.18MB |
| 03 Rule Induction/1. Rules vs. Decision Trees.mp4 | 120.57MB |
| 03 Rule Induction/2. Learning a Set of Rules.mp4 | 99.27MB |
| 03 Rule Induction/3. Estimating Probabilities from Small Samples.mp4 | 79.66MB |
| 03 Rule Induction/4. Learning Rules for Multiple Classes.mp4 | 44.81MB |
| 03 Rule Induction/5. First Order Rules.mp4 | 80.49MB |
| 03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4 | 196.01MB |
| 03 Rule Induction/7. Induction as Inverted Deduction.mp4 | 139.36MB |
| 03 Rule Induction/8. Inverting Propositional Resolution.mp4 | 72.18MB |
| 03 Rule Induction/9. Inverting First Order Resolution.mp4 | 156.32MB |
| 04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4 | 158.44MB |
| 04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4 | 102.88MB |
| 04 Instance-Based Learning/4. The Curse of Dimensionality.mp4 | 134.54MB |
| 04 Instance-Based Learning/5. Feature Selection and Weighting.mp4 | 101.38MB |
| 04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp4 | 99.27MB |
| 04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp4 | 55.17MB |
| 04 Instance-Based Learning/8. Locally Weighted Regression.mp4 | 40.42MB |
| 04 Instance-Based Learning/9. Radial Basis Function Networks.mp4 | 33.18MB |
| 04 Instance-Based Learning/10 Case-Based Reasoning.mp4 | 38.84MB |
| 04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp4 | 27.65MB |
| 04 Instance-Based Learning/12. Collaborative Filtering.mp4 | 156.04MB |
| 05 Bayesian Learning/1. Bayesian Methods.mp4 | 23.20MB |
| 05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4 | 202.65MB |
| 05 Bayesian Learning/3. Basic Probability Formulas.mp4 | 49.06MB |
| 05 Bayesian Learning/4. MAP Learning.mp4 | 106.29MB |
| 05 Bayesian Learning/5. Learning a Real-Valued Function.mp4 | 82.31MB |
| 05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp4 | 81.68MB |
| 05 Bayesian Learning/7. The Naive Bayes Classifier.mp4 | 196.14MB |
| 05 Bayesian Learning/8. Text Classification.mp4 | 92.70MB |
| 05 Bayesian Learning/9. Bayesian Networks.mp4 | 177.89MB |
| 05 Bayesian Learning/10. Inference in Bayesian Networks.mp4 | 33.87MB |
| 06 Neural Networks/1. Bayesian Network Review.mp4 | 19.35MB |
| 06 Neural Networks/2. Learning Bayesian Networks.mp4 | 32.67MB |
| 06 Neural Networks/3. The EM Algorithm.mp4 | 65.24MB |
| 06 Neural Networks/4. Example of EM.mp4 | 67.79MB |
| 06 Neural Networks/5. Learning Bayesian Network Structure.mp4 | 146.90MB |
| 06 Neural Networks/6. The Structural EM Algorithm.mp4 | 20.84MB |
| 06 Neural Networks/7. Reverse Engineering the Brain.mp4 | 61.86MB |
| 06 Neural Networks/8. Neural Network Driving a Car.mp4 | 113.74MB |
| 06 Neural Networks/9. How Neurons Work.mp4 | 66.01MB |
| 06 Neural Networks/10. The Perceptron.mp4 | 98.04MB |
| 06 Neural Networks/11. Perceptron Training.mp4 | 83.71MB |
| 06 Neural Networks/12. Gradient Descent.mp4 | 44.06MB |
| 07 Model Ensembles/1. Gradient Descent Continued.mp4 | 46.18MB |
| 07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4 | 56.58MB |
| 07 Model Ensembles/3. Stochastic Gradient Descent.mp4 | 33.78MB |
| 07 Model Ensembles/4. Multilayer Perceptrons.mp4 | 75.85MB |
| 07 Model Ensembles/5. Backpropagation.mp4 | 100.47MB |
| 07 Model Ensembles/6. Issues in Backpropagation.mp4 | 126.74MB |
| 07 Model Ensembles/7. Learning Hidden Layer Representations.mp4 | 71.28MB |
| 07 Model Ensembles/8. Expressiveness of Neural Networks.mp4 | 37.98MB |
| 07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4 | 51.31MB |
| 07 Model Ensembles/10. Model Ensembles.mp4 | 15.47MB |
| 07 Model Ensembles/11. Bagging.mp4 | 45.50MB |
| 07 Model Ensembles/12. Boosting- The Basics.mp4 | 40.82MB |
| 08 Learning Theory/1. Boosting- The Details.mp4 | 61.90MB |
| 08 Learning Theory/2. Error Correcting Output Coding.mp4 | 88.90MB |
| 08 Learning Theory/3. Stacking.mp4 | 88.03MB |
| 08 Learning Theory/4. Learning Theory.mp4 | 14.35MB |
| 08 Learning Theory/5. 'No Free Lunch' Theorems.mp4 | 89.69MB |
| 08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4 | 48.29MB |
| 08 Learning Theory/7. Bias and Variance.mp4 | 92.37MB |
| 08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4 | 31.72MB |
| 08 Learning Theory/9. General Bias Variance Decomposition.mp4 | 88.23MB |
| 08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4 | 32.38MB |
| 08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4 | 32.52MB |
| 08 Learning Theory/12. PAC Learning.mp4 | 50.19MB |
| 08 Learning Theory/13. How Many Examples Are Enough.mp4 | 114.03MB |
| 08 Learning Theory/14. Examples and Definition of PAC Learning.mp4 | 39.77MB |
| 09 Support Vector Machine/1. Agnostic Learning.mp4 | 102.72MB |
| 09 Support Vector Machine/2. VC Dimension.mp4 | 76.51MB |
| 09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp4 | 78.90MB |
| 09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4 | 9.74MB |
| 09 Support Vector Machine/5. Support Vector Machines.mp4 | 57.97MB |
| 09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4 | 103.62MB |
| 09 Support Vector Machine/7. Kernels.mp4 | 129.98MB |
| 09 Support Vector Machine/8. Learning SVMs.mp4 | 123.30MB |
| 09 Support Vector Machine/9. Constrained Optimization.mp4 | 147.60MB |
| 09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4 | 119.43MB |
| 09 Support Vector Machine/11. The SMO Algorithm.mp4 | 50.21MB |
| 10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp4 | 65.62MB |
| 10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp4 | 74.46MB |
| 10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp4 | 64.92MB |
| 10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp4 | 55.88MB |
| 10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4 | 117.03MB |
| 10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp4 | 43.66MB |
| 10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4 | 100.81MB |
| 10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp4 | 60.36MB |
| 10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp4 | 38.37MB |
| 10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4 | 112.26MB |
| 10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp4 | 58.65MB |
| 10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4 | 101.45MB |
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Machine Learning Pedro Domingos (113 files)
01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4 
