Ml class | 4C9577E80A76E69046AE4D74B51BCB1545D95F1E

Ml class

Name	ml-class	DOWNLOAD Copy Link
Total Size	1.9 GB
Total Files	183
Hash	4C9577E80A76E69046AE4D74B51BCB1545D95F1E

/Lecture Slides/
Lecture1.pdf	4.9 MB
Lecture1.pptx	4.8 MB
Lecture10.pdf	1.6 MB
Lecture10.pptx	3.5 MB
Lecture11.pdf	1.2 MB
Lecture11.pptx	7.5 MB
Lecture12.pdf	2.4 MB
Lecture12.pptx	5.6 MB
Lecture13.pdf	2.3 MB
Lecture13.pptx	2.9 MB
Lecture14.pdf	1.7 MB
Lecture14.pptx	3.8 MB
Lecture15.pdf	3.5 MB
Lecture15.pptx	6.3 MB
Lecture16.pdf	1.5 MB
Lecture16.pptx	3.8 MB
Lecture17.pdf	2.1 MB
Lecture17.pptx	4.0 MB
Lecture18.pdf	2.1 MB
Lecture18.pptx	6.4 MB
Lecture2.pdf	3.0 MB
Lecture2.pptx	5.6 MB
Lecture3.pdf	1.9 MB
Lecture3.pptx	5.2 MB
Lecture4.pdf	1.8 MB
Lecture4.pptx	4.6 MB
Lecture6.pdf	1.9 MB
Lecture6.pptx	3.4 MB
Lecture7.pdf	1.5 MB
Lecture7.pptx	2.7 MB
Lecture8.pdf	5.5 MB
Lecture8.pptx	6.6 MB
Lecture9.pdf	3.5 MB
Lecture9.pptx	5.2 MB
octave_session.m	5.3 KB
/
MSBNx.exe	11.4 MB
Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe	73.0 MB
Octave.Workshop.Installer.exe	122.8 MB
/Programming Exercises (completed)/
mlclass-ex1.rar	11.2 KB
mlclass-ex2.rar	15.6 KB
mlclass-ex3.rar	7.6 MB
mlclass-ex4.rar	7.6 MB
mlclass-ex5.rar	16.7 KB
mlclass-ex6.rar	582.2 KB
mlclass-ex7.rar	10.8 MB
mlclass-ex8.rar	560.0 KB
/Programming Exercises/
mlclass-ex1.zip	473.8 KB
mlclass-ex2.zip	245.7 KB
mlclass-ex3.zip	7.9 MB
mlclass-ex4.zip	8.0 MB
mlclass-ex5.zip	129.3 KB
mlclass-ex6.zip	915.7 KB
mlclass-ex7.zip	11.6 MB
mlclass-ex8.zip	811.4 KB
/Review Questions/
01 - Introduction.mht	291.6 KB
02 - Linear regression with one variable.mht	315.1 KB
03 - Linear Algebra.mht	317.4 KB
04 - Linear Regression with Multiple Variables.mht	309.6 KB
05 - Octave Tutorial.mht	300.5 KB
06 - Logistic Regression.mht	357.7 KB
07 - Regularization.mht	360.2 KB
08 - Neural Networks - Representation.mht	479.4 KB
09 - Neural Networks - Learning.mht	311.4 KB
10 - Advice for Applying Machine Learning.mht	329.4 KB
11 - Machine Learning System Design.mht	303.5 KB
12 - Support Vector Machines.mht	675.4 KB
13 - Clustering.mht	305.6 KB
14 - Dimensionality Reduction.mht	449.4 KB
15 - Anomaly Detection.mht	361.8 KB
16 - Recommender Systems.mht	324.2 KB
17 - Large Scale Machine Learning.mht	307.4 KB
18 - Application Example - Photo OCR.mht	461.9 KB
/Video Lectures/
01.1-V3-Introduction-Welcome.mp4	16.8 MB
01.2-V2-Introduction-WhatIsMachineLearning.mp4	12.0 MB
01.3-V2-Introduction-SupervisedLearning.mp4	14.3 MB
01.4-V2-Introduction-UnsupervisedLearning.mp4	19.1 MB
02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp4	9.9 MB
02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp4	10.1 MB
02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4	13.5 MB
02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4	14.6 MB
02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp4	15.7 MB
02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp4	14.6 MB
02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp4	14.2 MB
02.8-V2-What'sNext.mp4	6.4 MB
03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp4	10.3 MB
03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp4	8.2 MB
03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp4	16.7 MB
03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp4	14.5 MB
03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties.mp4	10.7 MB
03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp4	14.8 MB
04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp4	6.4 MB
04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp4	5.0 MB
04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp4	8.0 MB
04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp4	7.2 MB
04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp4	6.0 MB
04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp4	14.0 MB
04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp4	5.4 MB
05.1-OctaveTutorial-BasicOperations.mp4	21.7 MB
05.2-OctaveTutorial-MovingDataAround.mp4	26.7 MB
05.3-OctaveTutorial-ComputingOnData.mp4	10.9 MB
05.4-OctaveTutorial-PlottingData.mp4	11.9 MB
05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp4	20.6 MB
05.6-OctaveTutorial-Vectorization.mp4	17.6 MB
05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp4	7.6 MB
06.1-LogisticRegression-Classification.mp4	9.2 MB
06.2-LogisticRegression-HypothesisRepresentation.mp4	9.2 MB
06.3-LogisticRegression-DecisionBoundary.mp4	18.4 MB
06.4-LogisticRegression-CostFunction.mp4	14.8 MB
06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp4	13.7 MB
06.6-LogisticRegression-AdvancedOptimization.mp4	22.6 MB
06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp4	7.6 MB
07.1-Regularization-TheProblemOfOverfitting.mp4	12.5 MB
07.2-Regularization-CostFunction.mp4	13.0 MB
07.3-Regularization-RegularizedLinearRegression.mp4	13.4 MB
07.4-Regularization-RegularizedLogisticRegression.mp4	14.2 MB
08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp4	12.1 MB
08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp4	12.0 MB
08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp4	15.1 MB
08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp4	15.1 MB
08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp4	8.7 MB
08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp4	17.7 MB
08.7-NeuralNetworksRepresentation-MultiClassClassification.mp4	5.7 MB
09.1-NeuralNetworksLearning-CostFunction.mp4	8.5 MB
09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp4	15.8 MB
09.3-NeuralNetworksLearning-BackpropagationIntuition.mp4	18.0 MB
09.3-NeuralNetworksLearning-ImplementationNoteUnrollingParameters.mp4	11.1 MB
09.4-NeuralNetworksLearning-GradientChecking.mp4	15.5 MB
09.5-NeuralNetworksLearning-RandomInitialization.mp4	8.3 MB
09.7-NeuralNetworksLearning-PuttingItTogether.mp4	18.7 MB
09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp4	22.3 MB
10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp4	8.0 MB
10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp4	10.0 MB
10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp4	16.9 MB
10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp4	10.9 MB
10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp4	14.5 MB
10.6-AdviceForApplyingMachineLearning-LearningCurves.mp4	14.2 MB
10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp4	9.4 MB
11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp4	12.9 MB
11.2-MachineLearningSystemDesign-ErrorAnalysis.mp4	17.8 MB
11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp4	14.9 MB
11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp4	18.1 MB
11.5-MachineLearningSystemDesign-DataForMachineLearning.mp4	14.7 MB
12.1-SupportVectorMachines-OptimizationObjective.mp4	18.6 MB
12.2-SupportVectorMachines-LargeMarginIntuition.mp4	13.3 MB
12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp4	24.0 MB
12.4-SupportVectorMachines-KernelsI.mp4	19.7 MB
12.5-SupportVectorMachines-KernelsII.mp4	19.2 MB
12.6-SupportVectorMachines-UsingAnSVM.mp4	27.0 MB
14.1-Clustering-UnsupervisedLearningIntroduction.mp4	4.3 MB
14.2-Clustering-KMeansAlgorithm.mp4	16.1 MB
14.3-Clustering-OptimizationObjective.mp4	9.2 MB
14.4-Clustering-RandomInitialization.mp4	9.8 MB
14.5-Clustering-ChoosingTheNumberOfClusters.mp4	10.6 MB
15.1-DimensionalityReduction-MotivationIDataCompression.mp4	18.5 MB
15.2-DimensionalityReduction-MotivationIIVisualization.mp4	7.2 MB
15.3-DimensionalityReduction-PrincipalComponentAnalysisProblemFormulation.mp4	12.0 MB
15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp4	20.3 MB
15.5-DimensionalityReduction-ChoosingTheNumberOfPrincipalComponents.mp4	13.1 MB
15.6-DimensionalityReduction-ReconstructionFromCompressedRepresentation.mp4	6.2 MB
15.7-DimensionalityReduction-AdviceForApplyingPCA.mp4	16.6 MB
16.1-AnomalyDetection-ProblemMotivation-V1.mp4	9.3 MB
16.2-AnomalyDetection-GaussianDistribution.mp4	13.5 MB
16.3-AnomalyDetection-Algorithm.mp4	16.0 MB
16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp4	17.7 MB
16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp4	11.3 MB
16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp4	18.1 MB
16.8-AnomalyDetection-AnomalyDetectionUsingTheMultivariateGaussianDistribution-OPTIONAL.mp4	18.6 MB
17.1-RecommenderSystems-ProblemFormulation.mp4	14.3 MB
17.2-RecommenderSystems-ContentBasedRecommendations.mp4	19.6 MB
17.3-RecommenderSystems-CollaborativeFiltering-V1.mp4	13.7 MB
17.4-RecommenderSystems-CollaborativeFilteringAlgorithm.mp4	12.0 MB
17.6-RecommenderSystems-ImplementationalDetailMeanNormalization.mp4	11.0 MB
18.1-LargeScaleMachineLearning-LearningWithLargeDatasets.mp4	7.5 MB
18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp4	17.2 MB
18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp4	8.4 MB
18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp4	15.1 MB
18.5-LargeScaleMachineLearning-OnlineLearning.mp4	16.7 MB
18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp4	18.1 MB
19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp4	9.0 MB
19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp4	18.9 MB
19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp4	21.4 MB
19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp4	18.7 MB
20.1-Conclusion-SummaryAndThankYou.mp4	4.7 MB
Total files 183