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 |
Type: Course
Tags: Pedro Domingos, Machine Learning Course, University of Washington
Bibtex:
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= {}
}
