videos
size: 574.43 MB
www.unbeler.top/?s=1cmS9BD
|- PCA 9 Finding eigenvalues and eigenvectors.mp4 - 6.30 MB
|- PCA 8 Principal components = eigenvectors.mp4 - 10.10 MB
|- PCA 7 Why we maximize variance in PCA.mp4 - 3.80 MB
|- PCA 6 Principal component analysis.mp4 - 3.30 MB
|- PCA 5 Feature selection and feature extraction.mp4 - 3.10 MB
|- PCA 4 Tackling the curse of dimensionality.mp4 - 5.00 MB
|- PCA 3 The curse of dimensionality.mp4 - 3.60 MB
|- PCA 21 Pros and cons of dimensionality reduction.mp4 - 2.50 MB
|- PCA 20 Linear discriminant analysis.mp4 - 3.90 MB
|- PCA 2 Data manifolds in high-dimensional spaces.mp4 - 5.30 MB
|- PCA 19 Classification with PCA features.mp4 - 1.90 MB
|- PCA 18 When principal components fail.mp4 - 2.20 MB
|- PCA 17 Properties of eigenfaces.mp4 - 2.80 MB
|- PCA 16 Eigenface representation.mp4 - 4.70 MB
|- PCA 15 Eigen-faces.mp4 - 6.70 MB
|- PCA 14 Principal component analysis for the impatient.mp4 - 4.50 MB
|- PCA 13 How many principal components to use.mp4 - 4.60 MB
|- PCA 12 Eigenvalue = variance along eigenvector.mp4 - 6.80 MB
|- PCA 11 Eigenvector = direction of maximum variance.mp4 - 14.20 MB
|- PCA 10 Low-dimensional projections of data.mp4 - 2.80 MB
|- PCA 1 Real dimensionality vs observed dimensionality.mp4 - 5.30 MB
|- kNN.9 Number of nearest neighbors to use.mp4 - 4.30 MB
|- kNN.8 Nearest-neighbor regression example.mp4 - 4.90 MB
|- kNN.7 Nearest-neighbor regression algorithm.mp4 - 1.50 MB
|- kNN.6 MNIST digit recognition.mp4 - 4.80 MB
|- kNN.5 Nearest-neighbor classification algorithm.mp4 - 2.30 MB
|- kNN.4 Sensitivity to outliers.mp4 - 2.60 MB
|- kNN.3 Voronoi cells and decision boundary.mp4 - 3.50 MB
|- kNN.2 Intuition for the nearest-neighbor method.mp4 - 1.60 MB
|- kNN.17 Inverted index.mp4 - 5.40 MB
|- kNN.16 Locality sensitive hashing (LSH).mp4 - 9.20 MB
|- kNN.15 K-d tree algorithm.mp4 - 6.90 MB
|- kNN.14 Computational complexity of finding nearest-neighbors.mp4 - 4.20 MB
|- kNN.13 Pros and cons of nearest-neighbor methods.mp4 - 3.10 MB
|- kNN.12 Parzen windows, kernels and SVM.mp4 - 15.30 MB
|- kNN.11 Breaking ties between nearest neighbors.mp4 - 3.00 MB
|- kNN.10 Similarity distance measures.mp4 - 6.40 MB
|- kNN.1 Overview.mp4 - 514.00 kB
|- IAML8.9 Cross-validation.mp4 - 7.60 MB
|- IAML8.8 Why we need validation sets.mp4 - 4.00 MB
|- IAML8.7 Confidence interval for generalization.mp4 - 14.00 MB
|- IAML8.6 Estimating the generalization error.mp4 - 3.40 MB
|- IAML8.5 Generalization error.mp4 - 6.40 MB
|- IAML8.4 How to control overfitting.mp4 - 3.90 MB
|- IAML8.3 Examples of overfitting and underfitting.mp4 - 3.20 MB
|- IAML8.22 Correlation coefficient.mp4 - 2.40 MB
|- IAML8.21 Mean absolute error (MAE).mp4 - 1.80 MB
|- IAML8.20 Mean squared error and outliers.mp4 - 4.90 MB
|- IAML8.2 Overfitting and underfitting.mp4 - 4.50 MB
|- IAML8.19 Evaluating regression MSE, MAE, CC.mp4 - 5.00 MB
|- IAML8.18 Receiver Operating Characteristic (ROC) curve.mp4 - 17.10 MB
|- IAML8.17 Classification cost and utility.mp4 - 5.60 MB
|- IAML8.16 Recall, precision, miss and false alarm.mp4 - 5.00 MB
|- IAML8.15 When classification error is wrong.mp4 - 2.30 MB
|- IAML8.14 Classification error and accuracy.mp4 - 2.00 MB
|- IAML8.13 False positives and false negatives.mp4 - 3.80 MB
|- IAML8.12 Evaluating classification and regression.mp4 - 2.40 MB
|- IAML8.11 Stratified sampling.mp4 - 2.30 MB
|- IAML8.10 Leave-one-out cross-validation.mp4 - 8.40 MB
|- IAML8.1 Generalization in machine learning.mp4 - 2.00 MB
|- IAML7.9 Information gain ratio.mp4 - 4.00 MB
|- IAML7.8 Decision tree pruning.mp4 - 7.80 MB
|- IAML7.7 Overfitting in decision trees.mp4 - 4.90 MB
|- IAML7.6 Information gain.mp4 - 10.80 MB
|- IAML7.5 Decision tree entropy.mp4 - 6.60 MB
|- IAML7.4 Decision tree split purity.mp4 - 4.90 MB
|- IAML7.3 Quinlan s ID3 algorithm.mp4 - 4.10 MB
|- IAML7.2 Decision tree example.mp4 - 6.60 MB
|- IAML7.15 Summary.mp4 - 983.00 kB
|- IAML7.14 Random forest algorithm.mp4 - 2.60 MB
|- IAML7.13 Pros and cons of decision trees.mp4 - 4.60 MB
|- IAML7.12 Decision tree regression.mp4 - 4.90 MB
|- IAML7.11 Decision trees and real-valued data.mp4 - 3.70 MB
|- IAML7.10 Decision trees are DNF formulas.mp4 - 2.10 MB
|- IAML7.1 Decision Trees an introduction.mp4 - 5.70 MB
|- IAML5.9 Gaussian Naive Bayes classifier.mp4 - 9.00 MB
|- IAML5.8 Naive Bayes for real-valued data.mp4 - 3.60 MB
|- IAML5.7 Mutual independence vs conditional independence.mp4 - 5.10 MB
|- IAML5.6 Independence assumption in Naive Bayes.mp4 - 7.00 MB
|- IAML5.5 Probabilistic classifiers generative vs discriminative.mp4 - 3.20 MB
|- IAML5.4 Role of denominator in Naive Bayes.mp4 - 4.50 MB
|- IAML5.3 Class model and the prior.mp4 - 4.70 MB
|- IAML5.2 Bayesian classification.mp4 - 6.70 MB
|- IAML5.14 Missing values in Naive Bayes.mp4 - 23.70 MB
|- IAML5.13 The zero-frequency problem.mp4 - 18.50 MB
|- IAML5.12 Naive Bayes for spam detection.mp4 - 15.60 MB
|- IAML5.11 Example where Naive Bayes fails.mp4 - 15.00 MB
|- IAML5.10 Naive Bayes decision boundary.mp4 - 6.20 MB
|- IAML5.10 Naive Bayes decision boundary.mp4 - 9.00 MB
|- IAML5.1 Overview.mp4 - 820.00 kB
|- IAML19.7 Summary.mp4 - 609.00 kB
|- IAML19.6 Lance-Williams algorithm.mp4 - 15.50 MB
|- IAML19.5 Single-link, complete-link, Ward s method.mp4 - 10.70 MB
|- IAML19.4 Agglomerative clustering dendrogram.mp4 - 5.70 MB
|- IAML19.3 Agglomerative clustering (bottom-up).mp4 - 3.70 MB
|- IAML19.2 Divisive clustering (top-down).mp4 - 4.00 MB
|- IAML19.1 Flat vs hierachical clustering.mp4 - 5.60 MB
|- EM.6 Summary.mp4 - 5.30 MB
|- EM.5 How many Gaussians in the GMM.mp4 - 10.40 MB
|- EM.4 Gaussian mixture model (GMM).mp4 - 11.10 MB