University of Washington - Pedro Domingos - Machine Learning
Pedro Domingos



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Machine Learning Pedro Domingos (113 files)
01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4211.61MB
01 Introduction & Inductive learning/2. What Is Machine Learning.mp449.64MB
01 Introduction & Inductive learning/3. Applications of Machine Learning.mp476.12MB
01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4145.08MB
01 Introduction & Inductive learning/5. Types of Learning.mp473.11MB
01 Introduction & Inductive learning/6. Machine Learning In Practice.mp491.90MB
01 Introduction & Inductive learning/7. What Is Inductive Learning.mp429.44MB
01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp462.17MB
01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4191.37MB
01 Introduction & Inductive learning/1. Class Information.mp429.22MB
02 Decision Trees/1. Decision Trees.mp442.04MB
02 Decision Trees/2. What Can a Decision Tree Represent.mp428.01MB
02 Decision Trees/3. Growing a Decision Tree.mp429.14MB
02 Decision Trees/4. Accuracy and Information Gain.mp4146.73MB
02 Decision Trees/5. Learning with Non Boolean Features.mp442.81MB
02 Decision Trees/6. The Parity Problem.mp433.52MB
02 Decision Trees/7. Learning with Many Valued Attributes.mp441.32MB
02 Decision Trees/8. Learning with Missing Values.mp475.46MB
02 Decision Trees/9. The Overfitting Problem.mp451.53MB
02 Decision Trees/10. Decision Tree Pruning.mp4138.66MB
02 Decision Trees/11. Post Pruning Trees to Rules.mp4156.47MB
02 Decision Trees/12. Scaling Up Decision Tree Learning.mp451.18MB
03 Rule Induction/1. Rules vs. Decision Trees.mp4120.57MB
03 Rule Induction/2. Learning a Set of Rules.mp499.27MB
03 Rule Induction/3. Estimating Probabilities from Small Samples.mp479.66MB
03 Rule Induction/4. Learning Rules for Multiple Classes.mp444.81MB
03 Rule Induction/5. First Order Rules.mp480.49MB
03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4196.01MB
03 Rule Induction/7. Induction as Inverted Deduction.mp4139.36MB
03 Rule Induction/8. Inverting Propositional Resolution.mp472.18MB
03 Rule Induction/9. Inverting First Order Resolution.mp4156.32MB
04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4158.44MB
04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4102.88MB
04 Instance-Based Learning/4. The Curse of Dimensionality.mp4134.54MB
04 Instance-Based Learning/5. Feature Selection and Weighting.mp4101.38MB
04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp499.27MB
04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp455.17MB
04 Instance-Based Learning/8. Locally Weighted Regression.mp440.42MB
04 Instance-Based Learning/9. Radial Basis Function Networks.mp433.18MB
04 Instance-Based Learning/10 Case-Based Reasoning.mp438.84MB
04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp427.65MB
04 Instance-Based Learning/12. Collaborative Filtering.mp4156.04MB
05 Bayesian Learning/1. Bayesian Methods.mp423.20MB
05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4202.65MB
05 Bayesian Learning/3. Basic Probability Formulas.mp449.06MB
05 Bayesian Learning/4. MAP Learning.mp4106.29MB
05 Bayesian Learning/5. Learning a Real-Valued Function.mp482.31MB
05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp481.68MB
05 Bayesian Learning/7. The Naive Bayes Classifier.mp4196.14MB
05 Bayesian Learning/8. Text Classification.mp492.70MB
05 Bayesian Learning/9. Bayesian Networks.mp4177.89MB
05 Bayesian Learning/10. Inference in Bayesian Networks.mp433.87MB
06 Neural Networks/1. Bayesian Network Review.mp419.35MB
06 Neural Networks/2. Learning Bayesian Networks.mp432.67MB
06 Neural Networks/3. The EM Algorithm.mp465.24MB
06 Neural Networks/4. Example of EM.mp467.79MB
06 Neural Networks/5. Learning Bayesian Network Structure.mp4146.90MB
06 Neural Networks/6. The Structural EM Algorithm.mp420.84MB
06 Neural Networks/7. Reverse Engineering the Brain.mp461.86MB
06 Neural Networks/8. Neural Network Driving a Car.mp4113.74MB
06 Neural Networks/9. How Neurons Work.mp466.01MB
06 Neural Networks/10. The Perceptron.mp498.04MB
06 Neural Networks/11. Perceptron Training.mp483.71MB
06 Neural Networks/12. Gradient Descent.mp444.06MB
07 Model Ensembles/1. Gradient Descent Continued.mp446.18MB
07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp456.58MB
07 Model Ensembles/3. Stochastic Gradient Descent.mp433.78MB
07 Model Ensembles/4. Multilayer Perceptrons.mp475.85MB
07 Model Ensembles/5. Backpropagation.mp4100.47MB
07 Model Ensembles/6. Issues in Backpropagation.mp4126.74MB
07 Model Ensembles/7. Learning Hidden Layer Representations.mp471.28MB
07 Model Ensembles/8. Expressiveness of Neural Networks.mp437.98MB
07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp451.31MB
07 Model Ensembles/10. Model Ensembles.mp415.47MB
07 Model Ensembles/11. Bagging.mp445.50MB
07 Model Ensembles/12. Boosting- The Basics.mp440.82MB
08 Learning Theory/1. Boosting- The Details.mp461.90MB
08 Learning Theory/2. Error Correcting Output Coding.mp488.90MB
08 Learning Theory/3. Stacking.mp488.03MB
08 Learning Theory/4. Learning Theory.mp414.35MB
08 Learning Theory/5. 'No Free Lunch' Theorems.mp489.69MB
08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp448.29MB
08 Learning Theory/7. Bias and Variance.mp492.37MB
08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp431.72MB
08 Learning Theory/9. General Bias Variance Decomposition.mp488.23MB
08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp432.38MB
08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp432.52MB
08 Learning Theory/12. PAC Learning.mp450.19MB
08 Learning Theory/13. How Many Examples Are Enough.mp4114.03MB
08 Learning Theory/14. Examples and Definition of PAC Learning.mp439.77MB
09 Support Vector Machine/1. Agnostic Learning.mp4102.72MB
09 Support Vector Machine/2. VC Dimension.mp476.51MB
09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp478.90MB
09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp49.74MB
09 Support Vector Machine/5. Support Vector Machines.mp457.97MB
09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4103.62MB
09 Support Vector Machine/7. Kernels.mp4129.98MB
09 Support Vector Machine/8. Learning SVMs.mp4123.30MB
09 Support Vector Machine/9. Constrained Optimization.mp4147.60MB
09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4119.43MB
09 Support Vector Machine/11. The SMO Algorithm.mp450.21MB
10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp465.62MB
10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp474.46MB
10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp464.92MB
10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp455.88MB
10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4117.03MB
10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp443.66MB
10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4100.81MB
10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp460.36MB
10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp438.37MB
10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4112.26MB
10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp458.65MB
10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4101.45MB
Type: Course
Tags: Pedro Domingos, Machine Learning Course, University of Washington

Bibtex:
@article{,
title= {University of Washington - Pedro Domingos - Machine Learning},
keywords= {Pedro Domingos, Machine Learning Course, University of Washington},
journal= {},
author= {Pedro Domingos},
year= {},
url= {https://www.youtube.com/user/UWCSE/playlists?sort=dd&view=50&shelf_id=16},
license= {},
abstract= {Video Lecture of Course Data Mining & Machine Learning by Prof Pedro Domingos, University of Washington USA.},
superseded= {},
terms= {}
}