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Machine Learning Pedro Domingos

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File Name
Size
01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4
202 MB
01 Introduction & Inductive learning/2. What Is Machine Learning.mp4
47 MB
01 Introduction & Inductive learning/3. Applications of Machine Learning.mp4
73 MB
01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4
138 MB
01 Introduction & Inductive learning/5. Types of Learning.mp4
70 MB
01 Introduction & Inductive learning/6. Machine Learning In Practice.mp4
88 MB
01 Introduction & Inductive learning/7. What Is Inductive Learning.mp4
28 MB
01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp4
59 MB
01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4
182 MB
01 Introduction & Inductive learning/1. Class Information.mp4
28 MB
02 Decision Trees/1. Decision Trees.mp4
40 MB
02 Decision Trees/2. What Can a Decision Tree Represent.mp4
27 MB
02 Decision Trees/3. Growing a Decision Tree.mp4
28 MB
02 Decision Trees/4. Accuracy and Information Gain.mp4
140 MB
02 Decision Trees/5. Learning with Non Boolean Features.mp4
41 MB
02 Decision Trees/6. The Parity Problem.mp4
32 MB
02 Decision Trees/7. Learning with Many Valued Attributes.mp4
39 MB
02 Decision Trees/8. Learning with Missing Values.mp4
72 MB
02 Decision Trees/9. The Overfitting Problem.mp4
49 MB
02 Decision Trees/10. Decision Tree Pruning.mp4
132 MB
02 Decision Trees/11. Post Pruning Trees to Rules.mp4
149 MB
02 Decision Trees/12. Scaling Up Decision Tree Learning.mp4
49 MB
03 Rule Induction/1. Rules vs. Decision Trees.mp4
115 MB
03 Rule Induction/2. Learning a Set of Rules.mp4
95 MB
03 Rule Induction/3. Estimating Probabilities from Small Samples.mp4
76 MB
03 Rule Induction/4. Learning Rules for Multiple Classes.mp4
43 MB
03 Rule Induction/5. First Order Rules.mp4
77 MB
03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4
187 MB
03 Rule Induction/7. Induction as Inverted Deduction.mp4
133 MB
03 Rule Induction/8. Inverting Propositional Resolution.mp4
69 MB
03 Rule Induction/9. Inverting First Order Resolution.mp4
149 MB
04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4
151 MB
04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4
98 MB
04 Instance-Based Learning/4. The Curse of Dimensionality.mp4
128 MB
04 Instance-Based Learning/5. Feature Selection and Weighting.mp4
97 MB
04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp4
95 MB
04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp4
53 MB
04 Instance-Based Learning/8. Locally Weighted Regression.mp4
38 MB
04 Instance-Based Learning/9. Radial Basis Function Networks.mp4
32 MB
04 Instance-Based Learning/10 Case-Based Reasoning.mp4
37 MB
04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp4
26 MB
04 Instance-Based Learning/12. Collaborative Filtering.mp4
149 MB
05 Bayesian Learning/1. Bayesian Methods.mp4
22 MB
05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4
193 MB
05 Bayesian Learning/3. Basic Probability Formulas.mp4
47 MB
05 Bayesian Learning/4. MAP Learning.mp4
101 MB
05 Bayesian Learning/5. Learning a Real-Valued Function.mp4
78 MB
05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp4
78 MB
05 Bayesian Learning/7. The Naive Bayes Classifier.mp4
187 MB
05 Bayesian Learning/8. Text Classification.mp4
88 MB
05 Bayesian Learning/9. Bayesian Networks.mp4
170 MB
05 Bayesian Learning/10. Inference in Bayesian Networks.mp4
32 MB
06 Neural Networks/1. Bayesian Network Review.mp4
18 MB
06 Neural Networks/2. Learning Bayesian Networks.mp4
31 MB
06 Neural Networks/3. The EM Algorithm.mp4
62 MB
06 Neural Networks/4. Example of EM.mp4
65 MB
06 Neural Networks/5. Learning Bayesian Network Structure.mp4
140 MB
06 Neural Networks/6. The Structural EM Algorithm.mp4
20 MB
06 Neural Networks/7. Reverse Engineering the Brain.mp4
59 MB
06 Neural Networks/8. Neural Network Driving a Car.mp4
108 MB
06 Neural Networks/9. How Neurons Work.mp4
63 MB
06 Neural Networks/10. The Perceptron.mp4
94 MB
06 Neural Networks/11. Perceptron Training.mp4
80 MB
06 Neural Networks/12. Gradient Descent.mp4
42 MB
07 Model Ensembles/1. Gradient Descent Continued.mp4
44 MB
07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4
54 MB
07 Model Ensembles/3. Stochastic Gradient Descent.mp4
32 MB
07 Model Ensembles/4. Multilayer Perceptrons.mp4
72 MB
07 Model Ensembles/5. Backpropagation.mp4
96 MB
07 Model Ensembles/6. Issues in Backpropagation.mp4
121 MB
07 Model Ensembles/7. Learning Hidden Layer Representations.mp4
68 MB
07 Model Ensembles/8. Expressiveness of Neural Networks.mp4
36 MB
07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4
49 MB
07 Model Ensembles/10. Model Ensembles.mp4
15 MB
07 Model Ensembles/11. Bagging.mp4
43 MB
07 Model Ensembles/12. Boosting- The Basics.mp4
39 MB
08 Learning Theory/1. Boosting- The Details.mp4
59 MB
08 Learning Theory/2. Error Correcting Output Coding.mp4
85 MB
08 Learning Theory/3. Stacking.mp4
84 MB
08 Learning Theory/4. Learning Theory.mp4
14 MB
08 Learning Theory/5. 'No Free Lunch' Theorems.mp4
86 MB
08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4
46 MB
08 Learning Theory/7. Bias and Variance.mp4
88 MB
08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4
30 MB
08 Learning Theory/9. General Bias Variance Decomposition.mp4
84 MB
08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4
31 MB
08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4
31 MB
08 Learning Theory/12. PAC Learning.mp4
48 MB
08 Learning Theory/13. How Many Examples Are Enough.mp4
109 MB
08 Learning Theory/14. Examples and Definition of PAC Learning.mp4
38 MB
09 Support Vector Machine/1. Agnostic Learning.mp4
98 MB
09 Support Vector Machine/2. VC Dimension.mp4
73 MB
09 Support Vector Machine/3. VC Dimension of Hyperplanes.mp4
75 MB
09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4
9.3 MB
09 Support Vector Machine/5. Support Vector Machines.mp4
55 MB
09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4
99 MB
09 Support Vector Machine/7. Kernels.mp4
124 MB
09 Support Vector Machine/8. Learning SVMs.mp4
118 MB
09 Support Vector Machine/9. Constrained Optimization.mp4
141 MB
09 Support Vector Machine/10. Optimization with Inequality Constraints.mp4
114 MB
09 Support Vector Machine/11. The SMO Algorithm.mp4
48 MB
10 Clustering and Dimensionality Reduction/1. Handling Noisy Data in SVMs.mp4
63 MB
10 Clustering and Dimensionality Reduction/2. Generalization Bounds for SVMs.mp4
71 MB
10 Clustering and Dimensionality Reduction/3. Clustering and Dimensionality Reduction.mp4
62 MB
10 Clustering and Dimensionality Reduction/4. K-Means Clustering.mp4
53 MB
10 Clustering and Dimensionality Reduction/5. Mixture Models.mp4
112 MB
10 Clustering and Dimensionality Reduction/6. Mixtures of Gaussians.mp4
42 MB
10 Clustering and Dimensionality Reduction/7. EM Algorithm for Mixtures of Gaussians.mp4
96 MB
10 Clustering and Dimensionality Reduction/8. Mixture Models vs K-Means vs. Bayesian Networks.mp4
58 MB
10 Clustering and Dimensionality Reduction/9. Hierarchical Clustering.mp4
37 MB
10 Clustering and Dimensionality Reduction/10. Principal Components Analysis.mp4
107 MB
10 Clustering and Dimensionality Reduction/11. Multidimensional Scaling.mp4
56 MB
10 Clustering and Dimensionality Reduction/12. Nonlinear Dimensionality Reduction.mp4
97 MB