Machine Learning | 89BB6C39A682F248DD423495E0AEADA74ED70A94

Machine Learning

Name	Machine Learning	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.5 GB
Total Files	419
Hash	89BB6C39A682F248DD423495E0AEADA74ED70A94

/Assignments/IV/__MACOSX/
._mlclass-ex1-005	0.2 KB
/.../mlclass-ex1-005/
._ex1.pdf	0.2 KB
._mlclass-ex1	0.2 KB
/.../mlclass-ex1-005/mlclass-ex1/
._computeCost.m	0.2 KB
._computeCostMulti.m	0.2 KB
._ex1.m	0.2 KB
._ex1_multi.m	0.2 KB
._ex1data1.txt	0.2 KB
._ex1data2.txt	0.2 KB
._featureNormalize.m	0.2 KB
._gradientDescent.m	0.2 KB
._gradientDescentMulti.m	0.2 KB
._normalEqn.m	0.2 KB
._plotData.m	0.2 KB
._submit.m	0.2 KB
._submitWeb.m	0.2 KB
._warmUpExercise.m	0.2 KB
/Assignments/IV/
mlclass-ex1-005.zip	483.0 KB
/.../mlclass-ex1-005/
ex1.pdf	514.1 KB
/.../mlclass-ex1-005/mlclass-ex1/
computeCost.m	0.7 KB
computeCostMulti.m	0.7 KB
ex1.m	3.4 KB
ex1_multi.m	4.5 KB
ex1data1.txt	1.4 KB
ex1data2.txt	0.7 KB
featureNormalize.m	1.3 KB
gradientDescent.m	1.0 KB
gradientDescentMulti.m	1.1 KB
normalEqn.m	0.7 KB
plotData.m	0.9 KB
submit.m	17.3 KB
submitWeb.m	0.8 KB
warmUpExercise.m	0.5 KB
/Assignments/IX/__MACOSX/
._mlclass-ex4-005	0.2 KB
/.../mlclass-ex4-005/
._.DS_Store	0.1 KB
._ex4.pdf	0.2 KB
._mlclass-ex4	0.2 KB
/.../mlclass-ex4-005/mlclass-ex4/
._checkNNGradients.m	0.2 KB
._computeNumericalGradient.m	0.2 KB
._debugInitializeWeights.m	0.2 KB
._displayData.m	0.2 KB
._ex4.m	0.2 KB
._ex4data1.mat	0.2 KB
._ex4weights.mat	0.2 KB
._fmincg.m	0.2 KB
._nnCostFunction.m	0.2 KB
._predict.m	0.2 KB
._randInitializeWeights.m	0.2 KB
._sigmoid.m	0.2 KB
._sigmoidGradient.m	0.2 KB
._submit.m	0.2 KB
._submitWeb.m	0.2 KB
/.../mlclass-ex4-005/
.DS_Store	6.1 KB
ex4.pdf	392.6 KB
mlclass-ex4-005.zip	8.0 MB
/.../mlclass-ex4-005/mlclass-ex4/
checkNNGradients.m	2.0 KB
computeNumericalGradient.m	1.1 KB
debugInitializeWeights.m	0.8 KB
displayData.m	1.5 KB
ex4.m	8.1 KB
ex4data1.mat	7.5 MB
ex4weights.mat	79.6 KB
fmincg.m	8.7 KB
nnCostFunction.m	4.1 KB
predict.m	0.6 KB
randInitializeWeights.m	1.0 KB
sigmoid.m	0.1 KB
sigmoidGradient.m	0.8 KB
submit.m	17.1 KB
submitWeb.m	0.8 KB
/Assignments/IX/WebInfo/
Ex4 Tutorial - Forward and Back-propagation.htm	393.8 KB
/.../Ex4 Tutorial - Forward and Back-propagation_files/
1(1).png	12.2 KB
1.png	18.2 KB
13569_204305756345_554551345_4479361_7386622_n.jpg	0.6 KB
2.png	21.3 KB
204.min.js	6.5 KB
3.png	22.9 KB
ad.jpg	0.6 KB
course.css	0.2 KB
flexjoinLastChanceModal.html.js	3.5 KB
ga.js	40.1 KB
header(1).js	0.1 KB
header.html.js	26.9 KB
header.js	0.6 KB
logo	26.2 KB
markusFace.jpg	0.7 KB
MathJax.js	50.4 KB
require.js	24.1 KB
routes.js	358.5 KB
select2.css	17.2 KB
sidebar(1).js	0.1 KB
sidebar.html.js	7.3 KB
sidebar.js	1.6 KB
signature_track.js	4.2 KB
signatureTrackLastChanceModal.html.js	4.7 KB
spark.forum.hg.css	1.8 KB
spark.main.css	249.4 KB
student-page.html.js	1.1 KB
student-page.js	2.0 KB
university_logo	1.9 KB
util.js	23.8 KB
widgets.js	760.8 KB
/Assignments/VII/__MACOSX/
._mlclass-ex2-005	0.2 KB
/.../mlclass-ex2-005/
._ex2.pdf	0.2 KB
._mlclass-ex2	0.2 KB
/.../mlclass-ex2-005/mlclass-ex2/
._costFunction.m	0.2 KB
._costFunctionReg.m	0.2 KB
._ex2.m	0.2 KB
._ex2_reg.m	0.2 KB
._ex2data1.txt	0.2 KB
._ex2data2.txt	0.2 KB
._mapFeature.m	0.2 KB
._plotData.m	0.2 KB
._plotDecisionBoundary.m	0.2 KB
._predict.m	0.2 KB
._sigmoid.m	0.2 KB
._submit.m	0.2 KB
._submitWeb.m	0.2 KB
/Assignments/VII/
mlclass-ex2-005.zip	250.7 KB
/.../mlclass-ex2-005/
ex2.pdf	256.4 KB
ml_login_data.mat	0.3 KB
/.../mlclass-ex2-005/mlclass-ex2/
costFunction.m	1.0 KB
costFunctionReg.m	1.2 KB
ex2.m	3.7 KB
ex2_reg.m	3.0 KB
ex2data1.txt	3.8 KB
ex2data2.txt	2.2 KB
mapFeature.m	0.5 KB
plotData.m	0.7 KB
plotDecisionBoundary.m	1.5 KB
predict.m	0.8 KB
sigmoid.m	0.5 KB
submit.m	17.1 KB
submitWeb.m	0.8 KB
/Assignments/VIII/__MACOSX/
._mlclass-ex3-005	0.2 KB
/.../mlclass-ex3-005/
._.DS_Store	0.1 KB
._ex3.pdf	0.2 KB
._mlclass-ex3	0.2 KB
/.../mlclass-ex3-005/mlclass-ex3/
._displayData.m	0.2 KB
._ex3.m	0.2 KB
._ex3_nn.m	0.2 KB
._ex3data1.mat	0.2 KB
._ex3weights.mat	0.2 KB
._fmincg.m	0.2 KB
._lrCostFunction.m	0.2 KB
._oneVsAll.m	0.2 KB
._predict.m	0.2 KB
._predictOneVsAll.m	0.2 KB
._sigmoid.m	0.2 KB
._submit.m	0.2 KB
._submitWeb.m	0.2 KB
/Assignments/VIII/
mlclass-ex3-005.zip	7.9 MB
/.../mlclass-ex3-005/
.DS_Store	6.1 KB
ex3.pdf	330.4 KB
/.../mlclass-ex3-005/mlclass-ex3/
displayData.m	1.5 KB
ex3.m	2.1 KB
ex3_nn.m	2.6 KB
ex3data1.mat	7.5 MB
ex3weights.mat	79.6 KB
fmincg.m	8.7 KB
lrCostFunction.m	1.9 KB
ml_login_data.mat	0.3 KB
oneVsAll.m	2.2 KB
predict.m	1.2 KB
predictOneVsAll.m	1.6 KB
sigmoid.m	0.1 KB
submit.m	17.0 KB
submitWeb.m	0.8 KB
/Assignments/XI/__MACOSX/
._mlclass-ex5-005	0.2 KB
/.../mlclass-ex5-005/
._.DS_Store	0.1 KB
._ex5.pdf	0.2 KB
._mlclass-ex5	0.2 KB
/.../mlclass-ex5-005/mlclass-ex5/
._ex5.m	0.2 KB
._ex5data1.mat	0.2 KB
._featureNormalize.m	0.2 KB
._fmincg.m	0.2 KB
._learningCurve.m	0.2 KB
._linearRegCostFunction.m	0.2 KB
._plotFit.m	0.2 KB
._polyFeatures.m	0.2 KB
._submit.m	0.2 KB
._submitWeb.m	0.2 KB
._trainLinearReg.m	0.2 KB
._validationCurve.m	0.2 KB
/Assignments/XI/
mlclass-ex5-005.zip	183.5 KB
/.../mlclass-ex5-005/
.DS_Store	6.1 KB
ex5.pdf	185.9 KB
/.../mlclass-ex5-005/mlclass-ex5/
ex5.m	6.8 KB
ex5data1.mat	1.3 KB
featureNormalize.m	0.5 KB
fmincg.m	8.7 KB
learningCurve.m	2.6 KB
linearRegCostFunction.m	1.3 KB
plotFit.m	0.8 KB
polyFeatures.m	0.7 KB
sigmoid.m	0.5 KB
submit.m	17.2 KB
submitWeb.m	0.8 KB
trainLinearReg.m	0.7 KB
validationCurve.m	1.9 KB
/Assignments/XII/__MACOSX/
._mlclass-ex6-005	0.2 KB
/.../mlclass-ex6-005/
._.DS_Store	0.1 KB
._ex6.pdf	0.2 KB
._mlclass-ex6	0.2 KB
/.../mlclass-ex6-005/mlclass-ex6/
._dataset3Params.m	0.2 KB
._emailFeatures.m	0.2 KB
._emailSample1.txt	0.2 KB
._emailSample2.txt	0.2 KB
._ex6.m	0.2 KB
._ex6_spam.m	0.2 KB
._ex6data1.mat	0.2 KB
._ex6data2.mat	0.2 KB
._ex6data3.mat	0.2 KB
._gaussianKernel.m	0.2 KB
._getVocabList.m	0.2 KB
._linearKernel.m	0.2 KB
._plotData.m	0.2 KB
._porterStemmer.m	0.2 KB
._processEmail.m	0.2 KB
._readFile.m	0.2 KB
._spamSample1.txt	0.2 KB
._spamSample2.txt	0.2 KB
._spamTest.mat	0.2 KB
._spamTrain.mat	0.2 KB
._submit.m	0.2 KB
._submitWeb.m	0.2 KB
._svmPredict.m	0.2 KB
._svmTrain.m	0.2 KB
._visualizeBoundary.m	0.2 KB
._visualizeBoundaryLinear.m	0.2 KB
._vocab.txt	0.2 KB
/Assignments/XII/
mlclass-ex6-005.zip	928.1 KB
/.../mlclass-ex6-005/
.DS_Store	6.1 KB
ex6.pdf	359.4 KB
/.../mlclass-ex6-005/mlclass-ex6/
dataset3Params.m	1.7 KB
emailFeatures.m	2.1 KB
emailSample1.txt	0.4 KB
emailSample2.txt	1.3 KB
ex6.m	4.1 KB
ex6_spam.m	4.6 KB
ex6data1.mat	1.0 KB
ex6data2.mat	7.6 KB
ex6data3.mat	6.0 KB
gaussianKernel.m	0.7 KB
getVocabList.m	0.8 KB
linearKernel.m	0.3 KB
plotData.m	0.6 KB
porterStemmer.m	9.9 KB
processEmail.m	4.0 KB
readFile.m	0.4 KB
spamSample1.txt	0.7 KB
spamSample2.txt	0.2 KB
spamTest.mat	112.7 KB
spamTrain.mat	428.8 KB
submit.m	16.8 KB
submitWeb.m	0.8 KB
svmPredict.m	1.7 KB
svmTrain.m	6.0 KB
visualizeBoundary.m	0.7 KB
visualizeBoundaryLinear.m	0.4 KB
vocab.txt	20.2 KB
/.../mlclass-ex7-005/
.DS_Store	6.1 KB
ex7.pdf	748.7 KB
/.../mlclass-ex7-005/mlclass-ex7/
bird_small.mat	45.6 KB
bird_small.png	33.0 KB
computeCentroids.m	1.3 KB
displayData.m	1.5 KB
drawLine.m	0.2 KB
ex7.m	5.6 KB
ex7_pca.m	7.2 KB
ex7data1.mat	1.0 KB
ex7data2.mat	4.8 KB
ex7faces.mat	11.0 MB
featureNormalize.m	0.5 KB
findClosestCentroids.m	1.1 KB
kMeansInitCentroids.m	0.7 KB
pca.m	0.9 KB
plotDataPoints.m	0.4 KB
plotProgresskMeans.m	0.8 KB
projectData.m	1.0 KB
recoverData.m	1.1 KB
runkMeans.m	2.0 KB
submit.m	17.0 KB
submitWeb.m	0.8 KB
/Assignments/XIV/
XI.zip	11.6 MB
/Assignments/XVI/
mlclass-ex8-005.zip	820.2 KB
/.../mlclass-ex8-005/
.DS_Store	6.1 KB
ex8.pdf	269.7 KB
/.../mlclass-ex8-005/mlclass-ex8/
checkCostFunction.m	1.6 KB
cofiCostFunc.m	2.5 KB
computeNumericalGradient.m	1.1 KB
estimateGaussian.m	1.0 KB
ex8.m	3.8 KB
ex8_cofi.m	7.1 KB
ex8_movieParams.mat	201.2 KB
ex8_movies.mat	223.4 KB
ex8data1.mat	9.5 KB
ex8data2.mat	93.5 KB
fmincg.m	8.7 KB
loadMovieList.m	0.7 KB
movie_ids.txt	48.4 KB
multivariateGaussian.m	0.8 KB
normalizeRatings.m	0.5 KB
selectThreshold.m	1.4 KB
submit.m	17.5 KB
submitWeb.m	0.8 KB
visualizeFit.m	0.6 KB
/Lectures/
1 - 1 - Welcome (7 min).mp4	12.5 MB
1 - 2 - What is Machine Learning- (7 min).mp4	9.8 MB
1 - 3 - Supervised Learning (12 min).mp4	14.1 MB
1 - 4 - Unsupervised Learning (14 min).mp4	17.5 MB
10 - 1 - Deciding What to Try Next (6 min).mp4	7.2 MB
10 - 2 - Evaluating a Hypothesis (8 min).mp4	8.9 MB
10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4	14.8 MB
10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4	9.4 MB
10 - 5 - Regularization and Bias-Variance (11 min).mp4	13.2 MB
10 - 6 - Learning Curves (12 min).mp4	13.5 MB
10 - 7 - Deciding What to Do Next Revisited (7 min).mp4	8.6 MB
11 - 1 - Prioritizing What to Work On (10 min).mp4	11.7 MB
11 - 2 - Error Analysis (13 min).mp4	16.2 MB
11 - 3 - Error Metrics for Skewed Classes (12 min).mp4	13.9 MB
11 - 4 - Trading Off Precision and Recall (14 min).mp4	16.8 MB
11 - 5 - Data For Machine Learning (11 min).mp4	13.5 MB
12 - 1 - Optimization Objective (15 min).mp4	17.5 MB
12 - 2 - Large Margin Intuition (11 min).mp4	12.4 MB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4	22.9 MB
12 - 4 - Kernels I (16 min).mp4	18.4 MB
12 - 5 - Kernels II (16 min).mp4	18.3 MB
12 - 6 - Using An SVM (21 min).mp4	25.1 MB
13 - 1 - Unsupervised Learning- Introduction (3 min).mp4	4.0 MB
13 - 2 - K-Means Algorithm (13 min).mp4	14.5 MB
13 - 3 - Optimization Objective (7 min).mp4	8.5 MB
13 - 4 - Random Initialization (8 min).mp4	9.1 MB
13 - 5 - Choosing the Number of Clusters (8 min).mp4	9.9 MB
14 - 1 - Motivation I- Data Compression (10 min).mp4	15.0 MB
14 - 2 - Motivation II- Visualization (6 min).mp4	6.6 MB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4	11.0 MB
14 - 4 - Principal Component Analysis Algorithm (15 min).mp4	18.7 MB
14 - 5 - Choosing the Number of Principal Components (11 min).mp4	12.4 MB
14 - 6 - Reconstruction from Compressed Representation (4 min).mp4	5.2 MB
14 - 7 - Advice for Applying PCA (13 min).mp4	15.4 MB
15 - 1 - Problem Motivation (8 min).mp4	8.8 MB
15 - 2 - Gaussian Distribution (10 min).mp4	12.3 MB
15 - 3 - Algorithm (12 min).mp4	14.6 MB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4	15.9 MB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4	9.7 MB
15 - 6 - Choosing What Features to Use (12 min).mp4	14.8 MB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4	16.7 MB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4	17.1 MB
16 - 2 - Content Based Recommendations (15 min).mp4	17.8 MB
16 - 3 - Collaborative Filtering (10 min).mp4	12.3 MB
16 - 4 - Collaborative Filtering Algorithm (9 min).mp4	10.8 MB
16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp4	10.2 MB
16 - 6 - Implementational Detail- Mean Normalization (9 min).mp4	10.2 MB
17 - 1 - Learning With Large Datasets (6 min).mp4	6.8 MB
17 - 2 - Stochastic Gradient Descent (13 min).mp4	16.1 MB
17 - 3 - Mini-Batch Gradient Descent (6 min).mp4	7.7 MB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4	14.0 MB
17 - 5 - Online Learning (13 min).mp4	15.6 MB
17 - 6 - Map Reduce and Data Parallelism (14 min).mp4	16.8 MB
18 - 1 - Problem Description and Pipeline (7 min).mp4	8.3 MB
18 - 2 - Sliding Windows (15 min).mp4	17.3 MB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4	19.7 MB
18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4	16.9 MB
19 - 1 - Summary and Thank You (5 min).mp4	6.4 MB
2 - 1 - Model Representation (8 min).mp4	9.4 MB
2 - 2 - Cost Function (8 min).mp4	9.5 MB
2 - 3 - Cost Function - Intuition I (11 min).mp4	12.8 MB
2 - 4 - Cost Function - Intuition II (9 min).mp4	11.9 MB
2 - 5 - Gradient Descent (11 min).mp4	14.2 MB
2 - 6 - Gradient Descent Intuition (12 min).mp4	13.7 MB
2 - 7 - Gradient Descent For Linear Regression (10 min).mp4	12.8 MB
2 - 8 - What-'s Next (6 min).mp4	6.4 MB
3 - 1 - Matrices and Vectors (9 min).mp4	10.0 MB
3 - 2 - Addition and Scalar Multiplication (7 min).mp4	7.8 MB
3 - 3 - Matrix Vector Multiplication (14 min).mp4	15.7 MB
3 - 4 - Matrix Matrix Multiplication (11 min).mp4	13.2 MB
3 - 5 - Matrix Multiplication Properties (9 min).mp4	10.3 MB
3 - 6 - Inverse and Transpose (11 min).mp4	13.5 MB
4 - 1 - Multiple Features (8 min).mp4	9.3 MB
4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4	6.1 MB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4	9.9 MB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4	9.7 MB
4 - 5 - Features and Polynomial Regression (8 min).mp4	8.7 MB
4 - 6 - Normal Equation (16 min).mp4	18.0 MB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4	6.5 MB
5 - 1 - Basic Operations (14 min).mp4	18.6 MB
5 - 2 - Moving Data Around (16 min).mp4	21.8 MB
5 - 3 - Computing on Data (13 min).mp4	16.0 MB
5 - 4 - Plotting Data (10 min).mp4	14.0 MB
5 - 5 - Control Statements- for, while, if statements (13 min).mp4	17.3 MB
5 - 6 - Vectorization (14 min).mp4	16.9 MB
5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4	5.7 MB
6 - 1 - Classification (8 min).mp4	9.2 MB
6 - 2 - Hypothesis Representation (7 min).mp4	8.7 MB
6 - 3 - Decision Boundary (15 min).mp4	17.6 MB
6 - 4 - Cost Function (11 min).mp4	13.7 MB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4	12.5 MB
6 - 6 - Advanced Optimization (14 min).mp4	19.0 MB
6 - 7 - Multiclass Classification- One-vs-all (6 min).mp4	7.3 MB
7 - 1 - The Problem of Overfitting (10 min).mp4	11.7 MB
7 - 2 - Cost Function (10 min).mp4	12.2 MB
7 - 3 - Regularized Linear Regression (11 min) (1).mp4	12.6 MB
7 - 3 - Regularized Linear Regression (11 min).mp4	12.6 MB
7 - 4 - Regularized Logistic Regression (9 min).mp4	11.4 MB
8 - 1 - Non-linear Hypotheses (10 min).mp4	11.4 MB
8 - 2 - Neurons and the Brain (8 min).mp4	10.4 MB
8 - 3 - Model Representation I (12 min).mp4	14.2 MB
8 - 4 - Model Representation II (12 min).mp4	14.1 MB
8 - 5 - Examples and Intuitions I (7 min).mp4	8.3 MB
8 - 6 - Examples and Intuitions II (10 min).mp4	14.7 MB
8 - 7 - Multiclass Classification (4 min).mp4	5.1 MB
9 - 1 - Cost Function (7 min).mp4	8.0 MB
9 - 2 - Backpropagation Algorithm (12 min).mp4	14.6 MB
9 - 3 - Backpropagation Intuition (13 min).mp4	16.2 MB
9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp4	9.8 MB
9 - 5 - Gradient Checking (12 min).mp4	14.2 MB
9 - 7 - Putting It Together (14 min).mp4	17.1 MB
9 - 8 - Autonomous Driving (7 min).mp4	15.6 MB
Total files 419