Filename Size 01 Introduction & Inductive learning/1. Class Information.mp4 27.9 MB 01 Introduction & Inductive learning/10. A Framework for Studying Inductive Learning.mp4 201.8 MB 01 Introduction & Inductive learning/2. What Is Machine Learning.mp4 47.3 MB 01 Introduction & Inductive learning/3. Applications of Machine Learning.mp4 72.6 MB 01 Introduction & Inductive learning/4. Key Elements of Machine Learning.mp4 138.4 MB 01 Introduction & Inductive learning/5. Types of Learning.mp4 69.7 MB 01 Introduction & Inductive learning/6. Machine Learning In Practice.mp4 87.6 MB 01 Introduction & Inductive learning/7. What Is Inductive Learning.mp4 28.1 MB 01 Introduction & Inductive learning/8. When Should You Use Inductive Learning.mp4 59.3 MB 01 Introduction & Inductive learning/9. The Essence of Inductive Learning.mp4 182.5 MB 02 Decision Trees/1. Decision Trees.mp4 40.1 MB 02 Decision Trees/10. Decision Tree Pruning.mp4 132.2 MB 02 Decision Trees/11. Post Pruning Trees to Rules.mp4 149.2 MB 02 Decision Trees/12. Scaling Up Decision Tree Learning.mp4 48.8 MB 02 Decision Trees/2. What Can a Decision Tree Represent.mp4 26.7 MB 02 Decision Trees/3. Growing a Decision Tree.mp4 27.8 MB 02 Decision Trees/4. Accuracy and Information Gain.mp4 139.9 MB 02 Decision Trees/5. Learning with Non Boolean Features.mp4 40.8 MB 02 Decision Trees/6. The Parity Problem.mp4 32 MB 02 Decision Trees/7. Learning with Many Valued Attributes.mp4 39.4 MB 02 Decision Trees/8. Learning with Missing Values.mp4 72 MB 02 Decision Trees/9. The Overfitting Problem.mp4 49.1 MB 03 Rule Induction/1. Rules vs. Decision Trees.mp4 115 MB 03 Rule Induction/2. Learning a Set of Rules.mp4 94.7 MB 03 Rule Induction/3. Estimating Probabilities from Small Samples.mp4 76 MB 03 Rule Induction/4. Learning Rules for Multiple Classes.mp4 42.7 MB 03 Rule Induction/5. First Order Rules.mp4 76.8 MB 03 Rule Induction/6. Learning First Order Rules Using FOIL.mp4 186.9 MB 03 Rule Induction/7. Induction as Inverted Deduction.mp4 132.9 MB 03 Rule Induction/8. Inverting Propositional Resolution.mp4 68.8 MB 03 Rule Induction/9. Inverting First Order Resolution.mp4 149.1 MB 04 Instance-Based Learning/1. The K-Nearest Neighbor Algorithm.mp4 151.1 MB 04 Instance-Based Learning/10 Case-Based Reasoning.mp4 37 MB 04 Instance-Based Learning/11. Lazy vs. Eager Learning.mp4 26.4 MB 04 Instance-Based Learning/12. Collaborative Filtering.mp4 148.8 MB 04 Instance-Based Learning/2. Theoretical Guarantees on k-NN.mp4 98.1 MB 04 Instance-Based Learning/4. The Curse of Dimensionality.mp4 128.3 MB 04 Instance-Based Learning/5. Feature Selection and Weighting.mp4 96.7 MB 04 Instance-Based Learning/6. Reducing the Computational Cost of k-NN.mp4 94.7 MB 04 Instance-Based Learning/7. Avoiding Overfitting in k-NN.mp4 52.6 MB 04 Instance-Based Learning/8. Locally Weighted Regression.mp4 38.5 MB 04 Instance-Based Learning/9. Radial Basis Function Networks.mp4 31.6 MB 05 Bayesian Learning/1. Bayesian Methods.mp4 22.1 MB 05 Bayesian Learning/10. Inference in Bayesian Networks.mp4 32.3 MB 05 Bayesian Learning/2. Bayes' Theorem and MAP Hypotheses.mp4 193.3 MB 05 Bayesian Learning/3. Basic Probability Formulas.mp4 46.8 MB 05 Bayesian Learning/4. MAP Learning.mp4 101.4 MB 05 Bayesian Learning/5. Learning a Real-Valued Function.mp4 78.5 MB 05 Bayesian Learning/6. Bayes Optimal Classifier and Gibbs Classifier.mp4 77.9 MB 05 Bayesian Learning/7. The Naive Bayes Classifier.mp4 187.1 MB 05 Bayesian Learning/8. Text Classification.mp4 88.4 MB 05 Bayesian Learning/9. Bayesian Networks.mp4 169.6 MB 06 Neural Networks/1. Bayesian Network Review.mp4 18.5 MB 06 Neural Networks/10. The Perceptron.mp4 93.5 MB 06 Neural Networks/11. Perceptron Training.mp4 79.8 MB 06 Neural Networks/12. Gradient Descent.mp4 42 MB 06 Neural Networks/2. Learning Bayesian Networks.mp4 31.2 MB 06 Neural Networks/3. The EM Algorithm.mp4 62.2 MB 06 Neural Networks/4. Example of EM.mp4 64.6 MB 06 Neural Networks/5. Learning Bayesian Network Structure.mp4 140.1 MB 06 Neural Networks/6. The Structural EM Algorithm.mp4 19.9 MB 06 Neural Networks/7. Reverse Engineering the Brain.mp4 59 MB 06 Neural Networks/8. Neural Network Driving a Car.mp4 108.5 MB 06 Neural Networks/9. How Neurons Work.mp4 63 MB 07 Model Ensembles/1. Gradient Descent Continued.mp4 44 MB 07 Model Ensembles/10. Model Ensembles.mp4 14.8 MB 07 Model Ensembles/11. Bagging.mp4 43.4 MB 07 Model Ensembles/12. Boosting- The Basics.mp4 38.9 MB 07 Model Ensembles/2. Gradient Descent vs Perceptron Training.mp4 54 MB 07 Model Ensembles/3. Stochastic Gradient Descent.mp4 32.2 MB 07 Model Ensembles/4. Multilayer Perceptrons.mp4 72.3 MB 07 Model Ensembles/5. Backpropagation.mp4 95.8 MB 07 Model Ensembles/6. Issues in Backpropagation.mp4 120.9 MB 07 Model Ensembles/7. Learning Hidden Layer Representations.mp4 68 MB 07 Model Ensembles/8. Expressiveness of Neural Networks.mp4 36.2 MB 07 Model Ensembles/9. Avoiding Overfitting in Neural Networks.mp4 48.9 MB 08 Learning Theory/1. Boosting- The Details.mp4 59 MB 08 Learning Theory/10. Bias-Variance Decomposition for Zer -One Loss.mp4 30.9 MB 08 Learning Theory/11. Bias and Variance for Other Loss Functions.mp4 31 MB 08 Learning Theory/12. PAC Learning.mp4 47.9 MB 08 Learning Theory/13. How Many Examples Are Enough.mp4 108.7 MB 08 Learning Theory/14. Examples and Definition of PAC Learning.mp4 37.9 MB 08 Learning Theory/2. Error Correcting Output Coding.mp4 84.8 MB 08 Learning Theory/3. Stacking.mp4 84 MB 08 Learning Theory/4. Learning Theory.mp4 13.7 MB 08 Learning Theory/5. 'No Free Lunch' Theorems.mp4 85.5 MB 08 Learning Theory/6. Practical Consequences of 'No Free Lunch'.mp4 46.1 MB 08 Learning Theory/7. Bias and Variance.mp4 88.1 MB 08 Learning Theory/8. Bias Variance Decomposition for Squared Loss.mp4 30.3 MB 08 Learning Theory/9. General Bias Variance Decomposition.mp4 84.1 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 75.2 MB 09 Support Vector Machine/4. Sample Complexity from VC Dimension.mp4 9.3 MB 09 Support Vector Machine/5. Support Vector Machines.mp4 55.3 MB 09 Support Vector Machine/6. Perceptrons as Instance-Based Learning.mp4 98.8 MB 09 Support Vector Machine/7. Kernels.mp4 124 MB 09 Support Vector Machine/8. Learning SVMs.mp4 117.6 MB 09 Support Vector Machine/9. Constrained Optimization.mp4 140.8 MB
