University of Washington - Pedro Domingos - Machine Learning
Pedro Domingos

Machine Learning Pedro Domingos (113 files)
01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4 211.61MB
10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4 101.45MB
10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp4 58.65MB
10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4 112.26MB
10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp4 38.37MB
10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp4 60.36MB
10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4 100.81MB
10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp4 43.66MB
10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4 117.03MB
10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp4 55.88MB
10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp4 64.92MB
10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp4 74.46MB
10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp4 65.62MB
09 Support Vector Machine/11. The SMO Algorithm.mp4 50.21MB
09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4 119.43MB
09 Support Vector Machine/9. Constrained Optimization.mp4 147.60MB
09 Support Vector Machine/8. Learning SVMs.mp4 123.30MB
09 Support Vector Machine/7. Kernels.mp4 129.98MB
09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4 103.62MB
09 Support Vector Machine/5. Support Vector Machines.mp4 57.97MB
09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4 9.74MB
09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp4 78.90MB
09 Support Vector Machine/2. VC Dimension.mp4 76.51MB
09 Support Vector Machine/1. Agnostic Learning.mp4 102.72MB
08 Learning Theory/14. Examples and Definition of PAC Learning.mp4 39.77MB
08 Learning Theory/13. How Many Examples Are Enough.mp4 114.03MB
08 Learning Theory/12. PAC Learning.mp4 50.19MB
08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4 32.52MB
08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4 32.38MB
08 Learning Theory/9. General Bias Variance Decomposition.mp4 88.23MB
08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4 31.72MB
08 Learning Theory/7. Bias and Variance.mp4 92.37MB
08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4 48.29MB
08 Learning Theory/5. 'No Free Lunch' Theorems.mp4 89.69MB
08 Learning Theory/4. Learning Theory.mp4 14.35MB
08 Learning Theory/3. Stacking.mp4 88.03MB
08 Learning Theory/2. Error Correcting Output Coding.mp4 88.90MB
08 Learning Theory/1. Boosting- The Details.mp4 61.90MB
07 Model Ensembles/12. Boosting- The Basics.mp4 40.82MB
07 Model Ensembles/11. Bagging.mp4 45.50MB
07 Model Ensembles/10. Model Ensembles.mp4 15.47MB
07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4 51.31MB
07 Model Ensembles/8. Expressiveness of Neural Networks.mp4 37.98MB
07 Model Ensembles/7. Learning Hidden Layer Representations.mp4 71.28MB
07 Model Ensembles/6. Issues in Backpropagation.mp4 126.74MB
07 Model Ensembles/5. Backpropagation.mp4 100.47MB
07 Model Ensembles/4. Multilayer Perceptrons.mp4 75.85MB
07 Model Ensembles/3. Stochastic Gradient Descent.mp4 33.78MB
07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4 56.58MB
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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= {}
}


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