günstig die möglichst allumfassend sein sollen. Online Zahlungsverkehr Front End Somit kann das Angebot eines Onlineshops gleich gut auf einem PC über den Webbrowser Suchmaschinenmarketing Bargeld Plugins sind zusätzliche Softwareerweiterungen Rückerstattung 493 Gain........................................................................ Information and Entropy 16.4 490 Neighbors............................................................................... Nearest Finding 16.3 489 Functions..................................................................................... Special Some 16.2 487 Matrix............................................... Symmetric A Approximating 16.1.2 486 Decomposition................................................. Value Singular The 16.1.1 485 Matrices....................................................................... about Material Useful 16.1 485 Resources 16 484  , Background Mathematical Some V 481 to.................................................................................................... able be 15.5.4 481 facts:........................................................................... these remember 15.5.3 481 terms......................................................................... these remember 15.5.2 481 definitions:.............................................................. these remember 15.5.1 481 should............................................................................................................... You 15.5 478 Errors..................................... Communication Simple Example:  , 15.4.4 478 Formalities....................... HMM's: for Programming Dynamic 15.4.3 474 Trellis.................................................... a with Inference Picturing 15.4.2 474 Models........................................................................ Markov Hidden 15.4.1 473 Programming............................. Dynamic and Models Markov Hidden 15.4 472 Chain................ Markov a Simulating by Web the Ranking Example: 15.3 469 Chains.................................................................. Markov Simulating 15.2.3 467 Variables..................................... Random as Results Simulation 15.2.2 465 Simulation.................................................................................................. 15.2.1 465 Chains.................................................... Markov of Properties Estimating 15.2 462 Text...................................... of Models Chain Markov Example: 15.1.3 459 Distributions....................................................................... Stationary 15.1.2 457 Matrices........................................................ Probability Transition 15.1.1 454 Chains........................................................................................................ Markov 15.1 454  , Models Markov Hidden and Chains Markov 15 444 procedures:............................................................. these remember 14.4.4 444 facts:........................................................................... these remember 14.4.3 444 . . . . . 444 . . . . . 444 . . . . . . . . . . . . . . . . . . . . . . terms: these remember 14.4.2 . . . . . . . . . . . . . . definitions: these remember 14.4.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . should You 14.4 441 Images.................... Whole with Holes Large Filling Example: 14.3.2 441 Number............ a than More Predict to Neighbors your Using 14.3.1 Neighbors Your Exploiting  , 14.3 435 Regressions...................................................... Linear Regularizing 14.2.4 433 Variable........................................ Explanatory One of Functions 14.2.3 431 Impact............................ Significant have Points Data Problem 14.2.2 428 Variables........................................................................ Transforming 14.2.1 427 Regressions............................................................. Linear Good Producing 14.2 424 R-squared.................................................................................................... 14.1.5 424 Residuals.....................................................................................................  , 14.1.4 423 Problem................................................ Squares Least the Solving 14.1.3 422 beta.................................................................................................. Choosing 14.1.2 421 Regression................................................................................... Linear 14.1.1 421 Squares.......................................................... Least and Regression Linear 14.1 419 Trends.................................................................. Spot to Regression 14.1.2 417 Predictions....................................................... Make to Regression 14.1.1 417  , Regression 14 413 procedures............................................................................. these use 13.7.4 413 facts:........................................................................... these remember 13.7.3 413 terms......................................................................... these remember 13.7.2 413 definitions:.............................................................. these remember 13.7.1 413 should............................................................................................................... You 13.7 409 Data............................... Accelerometer from Activity Example: 13.6.4 409 Means.......................... K Hierarchical and Clustering Efficient 13.6.3 406 Portugal....................................................... in Groceries Example: 13.6.2 404 Quantization............................................................................... Vector 13.6.1 403 Quantization...................................... Vector with Repetition Describing 13.6 402 Model.......................................................................................... Topic A 13.5.1 401 Documents........................................... Clustering Example: Application 13.5 400 K-Mediods.................................................................................................. 13.4.4 400 K-Means....................................................... on Comments General 13.4.3 397 Assignment....................................................................................... Soft 13.4.2 395 K...................................................................................... choose to How 13.4.1 392 Variants......................................................... and Algorithm K-Means The  , 13.4 391 Distance....................................................................... and Clustering 13.3.1 389 Clustering........................................................ Divisive and Agglomerative 13.3 388 Ellipses.............................. Covariance Gaussian: 2D a Plotting 13.2.2 387 Gaussians.......................................... and Transformations Affine 13.2.1 387 Distribution......................................................... Normal Multivariate The 13.2 386 Dimension.................................................................. of Banes Minor 13.1.2 384 is............................. it Think You Where isn't Data Curse: The 13.1.1 384 Dimension..................................................................................... of Curse The 13.1 379 to.................................................................................................... able be 12.6.5 379 procedures............................................................................. these use 12.6.4 379 facts:........................................................................... these remember 12.6.3 378 terms......................................................................... these remember 12.6.2 378 definitions:.............................................................. these remember 12.6.1 378 should............................................................................................................... You 12.6 375 Forest........................... Decision a with Items Data Classifying 12.5.5 374 Forest.................................. Decision a Evaluating and Building 12.5.4 373 Forests......................................................................................................... 12.5.3 370 Gain........................................ Information with Split a Choosing 12.5.2 367 Tree..................................................................... Decision a Building 12.5.1 367 Forests................................................................... Random with Classifying 12.5 366 SVMs.............................................. with Classification Multi-Class 12.4.5 363 Descent Gradient Stochastic with SVM an Training Example: 12.4.4 . . . . Descent Gradient Stochastic Minimum: a Finding 12.4.3 . . . . . . . . . . . . Points General Minimum: a Finding 12.4.2 . . . . . . . . . Loss Hinge the with Classifier a Choosing 12.4.1 Support The 12.4 . . . . . . . . . . . . . . . . . . . . . . . . . Data Missing 12.3.1 . . . . . . . . . . . . . . . . . . . . Bayes Naive with Classifying 12.3 . . . . . . . . . . . . . . . . . Neighbors Nearest with Classifying 12.2 . . . . . . . . . . . . . . . Well? Working Classifier the Is 12.1.4 . . . . . . . . . . . . . . . . . . . . . . . Cross-Validation 12.1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . Overfitting 12.1.2 . . . . . . . . . . . . . . . . . . . . . . . . Rate Error The 12.1.1 . . . . . . . . . . . . . . . . . . . . Ideas Big The Classification: 12.1 Classify to Learning 12 . . . . . . . . . . . . . . . . . . . . . . . . . . . to: able be 11.6.5 . . . . . . . . . . . . . . . . . . . . . procedures: these use 11.6.4 . . . . . . . . . . . . . . . . . . . . , facts: these remember 11.6.3 . . . . . . . . . . . . . . . . . . . . terms: these remember 11.6.2 . . . . . . . . . . . . . . . . . definitions: these remember 11.6.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . should You 11.6 . . . . . . . . . . . Weight and Height Understanding Example: 11.5 . . . . Scaling Multidimensional with Mapping Example: 11.4.3 . . . . . . . . . . . . . . Matrix Dot-Product a Factoring 11.4.2 . . . . . . Distances D High using Points D Low Choosing 11.4.1 . . . . . . . . . . . . . . . . . . . . . . Scaling Multi-Dimensional 11.4 Components Principal with Faces Representing Example: 11.3.2 Components Principal with Colors Representing Example: 11.3.1 . . . . . . . . . . . . . . . . . . . Analysis Components Principal 11.3 . . . . . Blob Height-Weight the Transforming Example: 11.2.5 . . . . . . . . . . . . . . . . . . . . Blobs Approximating 11.2.4 . . . . . . . . Blobs Rotating by Covariance Diagonalizing 11.2.3 . . . . . . . . . . . . . . Diagonalization and Eigenvectors 361 . . . 359 . . 358 . . 357 . . 355 . . 353 . . 351 . . 351 . . 350 . . 350 . . 349 . . 349 345 . . 345 . . 345 . . 345 . . 345 . . 345 . . 341 . . 339 . . 338 . . 335 . . 335 . . 334 . . 332 . 329 . . 327 . . 326 . . 325 . . 324 . . 11.2.2 322 Transformations............... Affine under Covariance and Mean 11.2.1 321 . Data Dimensional High Understand to Covariance and Mean Using 11.2 319 Matrix......................................................................... Covariance The 11.1.4 317 Covariance.................................................................................................. 11.1.3 315 Matrices.............................................. Scatterplot and Plots Stem 11.1.2 314 Mean................................................................................................... The 11.1.1 313 Plots........................................................................... Simple and Summaries 11.1 313 Dimensions High in Relationships Important Extracting 11 312 Tools <,IV 304 to.................................................................................................... able be 10.4.5 304 procedures............................................................................. these use 10.4.4 304 facts:........................................................................... these remember 10.4.3 303 terms......................................................................... these remember 10.4.2 303 definitions:.............................................................. these remember 10.4.1 303 should............................................................................................................... You 10.4 300 Filtering...................................................................................................... 10.3.3 297 Posterior Normal Yield Likelihood Normal and Prior Normal 10.3.2 296 Borehole................................... a of Depth Measuring Example: 10.3.1 296 Distributions............................................ Normal for Inference Bayesian 10.3 296 Inference................................................. Bayesian about Cautions 10.2.3 294 Inference......................................................................................... MAP 10.2.2 292 Conjugacy................................................................................................... 10.2.1 289 Inference.......................................... Bayesian with Priors Incorporating 10.2 288 Likelihood............................................ Maximum about Cautions 10.1.5 286 Parameters................................ Model for Intervals Confidence 10.1.4 281 Distributions................................................... Normal and Poisson 10.1.3 278 Distributions................ Multinomial and Geometric Binomial, 10.1.2 277 Principle............................................... Likelihood Maximum The 10.1.1 275 Likelihood.................. Maximum with Parameters Model Estimating 10.1 275  , Data from Models Probability Inferring 10 274 Experiments.......................................................................... Two-Way 9.3.6 272 to.................................................................................................... able be 9.3.5 272 procedures............................................................................. these use 9.3.4 272 facts:........................................................................... these remember 9.3.3 272 terms......................................................................... these remember 9.3.2 272 definitions:.............................................................. these remember 9.3.1 272 should............................................................................................................... You 9.3 267 Table.............................................................. ANOVA an up Setting 9.2.4 266 Treatment................................................................. a of Effects The 9.2.3 265 Effects................................................................ Between Interaction 9.2.2 264 Error........................................................................ the Decomposing  , 9.2.1 261 Experiments.................................................................................... Factor Two 9.2 259 Differences.......................................................................... Significant 9.1.6 257 Experiments.................................................................... Unbalanced 9.1.5 255 Table.................................................................................. ANOVA The 9.1.4 253 Variance......................................................... Noise the Estimating 9.1.3 253 Predictions.................................................. in Error Decomposing   9.1.2 252 Experiments............................................... Balanced Randomized 9.1.1 251 Treatment.................................. a of Effect The Experiment: Simple A 9.1 251  , Experiments 9 246 . . . . . . . . . . . . . . . . . 246 . . . . . . . . . . . . . . . . . 246 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . to: able be 8.5.5 . . . . . . procedures: these use 8.5.4 . . . . . facts: these remember 8.5.3 remember 8.5.2 246 definitions:.............................................................. these remember 8.5.1 246 should............................................................................................................... You 8.5 244 Behavior............................................................................................. Dangerous 8.4 239 Fit............................................................................ Model of Tests 2 8.3.2 237 Deviations...................................................... Standard and F-tests 8.3.1 237 Significance................................................................. of Tests Useful Other 8.3 235 Deviation Standard Population Unknown Different, Assuming 8.2.3 233 . Deviation Standard Population Unknown Same, Assuming 8.2.2 231 Deviations................... Standard Population Known Assuming 8.2.1 230 Populations.................................................. Two of Mean the Comparing 8.2 225 P-values....................................................................................................... 8.1.2 223 Significance......................................................................... Evaluating 8.1.1 222 Significance.............................................................................................................. 8.1 221 Evidence of Significance The 8 216 to.................................................................................................... able be 7.3.5 216 procedures............................................................................. these use 7.3.4 216 facts:........................................................................... these remember 7.3.3 216 terms......................................................................... these remember 7.3.2 216 definitions:.............................................................. these remember 7.3.1 216 should............................................................................................................... You 7.3 212 Simulation................................. from Estimates Error Standard 7.2.5 208 Means................................. Population for Intervals Confidence <,7.2.4 206 Mean..................... Sample the of Distribution Probability The 7.2.3 204 Mean............................ Sample the of Variance the Estimating 7.2.2 203 Intervals.................................................. Confidence Constructing 7.2.1 203 Intervals............................................................................................ Confidence 7.2 202 Populations................................................. Like are Distributions 7.1.4 201 Works............................................................ Model Urn The When 7.1.3 198 Mean.................................................. Sample the of Variance The 7.1.2 197 . . Mean Population the of Estimate an is Mean Sample The 7.1.1 197 Mean................................................................................................. Sample The 7.1 197 Populations and Samples 7 196 , Inference III 188 . . . 188 . . . 188 . . . 188 . . . 188 . . . . . . . . . . . . . . . . . . . . . points: these remember 6.5.4 . . . . . . . . . . . . . . . . . . . facts: these remember 6.5.3 . . . . . . . . . . . . . . . . . . . terms: these remember 6.5.2 . . . . . . . . . . . . . . . . definitions: these remember 6.5.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . should You 6.5 187 Distribution Binomial the to Approximation Normal a Using Normal6.4.3 Getting 6.4.2 183 N....................................................................................................... Large 6.4.1 182 N Large with Binomials Approximating 6.4 180 Distribution......................................... Normal The of Properties 6.3.3 179 Distribution..................................................................... Normal The 6.3.2 178 Distribution................................................. Normal Standard The 6.3.1 178 , Distribution Normal The 6.3 176 Distribution............................................................ Exponential The 6.2.4 176 Distribution..................................................................... Gamma The 6.2.3 174 Distribution........................................................................... Beta The 6.2.2 174 Distribution........................................... Uniform Continuous The 6.2.1 174 , Distributions Continuous 6.2 172 Distribution..................................................................... Poisson The 6.1.6 171 probabilities..................................................................... Multinomial 6.1.5 169 Distribution........................................... Probability Binomial The 6.1.4 168 Distribution................................................................ Geometric The 6.1.3 168 Variables............................................................... Random Bernoulli 6.1.2 167 Distribution................................................. Uniform Discrete The 6.1.1 167 Distributions Discrete 6.1 167 , Distributions Probability Useful 6 160 to.................................................................................................... able be 5.5.4 159 facts.......................................................... these remember and use 5.5.3 159 terms......................................................................... these remember 5.5.2 159 definitions:.............................................................. these remember 5.5.1 159 should................................................................................... You 5.5 156 Utility 5.4.5 154 . . Expectations and Trees Decision with Decision a Making 5.4.4 154 Early Game a Ending 5.4.3 152 Diversion Cultural a - Bookmaking and Expectations Odds, 5.4.2 151 bet?..................................................................... a accept you Should 5.4.1 151 Numbers Large of Law Weak the Using 5.4 149 Numbers.................................................. Large of Law Weak The 5.3.4 147 . . . . . . . . . . . . . . . . . . . . . Inequalities the Proving 5.3.3 146 .. . . . . . . . . . . . . . . . . . . . . . . . Inequalities Two 5.3.2 145 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samples IID 5.3.1 145 Numbers................................................................ Large of Law Weak The 5.3 145 Statistics................................................................. and Expectations 5.2.3 141 Covariance....................................................... and Variance Mean, 5.2.2 138 Values...................................................................................... Expected 5.2.1 137 Values................................................................ Expected and Expectations 5.2 134 Probability............................................... Continuous Little a Just 5.1.2 131 . . . Variables Random for Probability Conditional and Joint 5.1.1 128 Variables................................................................................................. Random 5.1 128 Expectations and Variables Random 5 121 to.................................................................................................... able be 4.6.5 121 points:....................................................................... these remember 4.6.4 121 facts.......................................................... these use and remember 4.6.3 121 terms......................................................................... these remember 4.6.2 121 definitions:.............................................................. these remember 4.6.1 121 should............................................................................................................... You 4.6 117 Probability......................................................................... Conditional 4.5.4 115 Independence........................................................................................... 4.5.3 114 Events.......................................................................................................... 4.5.2 112 Probability................................................................... and Outcomes 4.5.1 112 Examples.................................................................................... Worked Extra 4.5 110 Problem................................................ Hall Monty The Example: 4.4.5 108 Fallacy Prosecutor's The 4.4.4 106 . Independence of Forms Various and Probability Conditional 4.4.3 104 Hard......................................................... is Events Rare Detecting 4.4.2 100 Probabilities.............................................. Conditional Evaluating 4.4.1 99 ........................................................ Conditional 4.4 96 Overbooking............................................................ Airline Example: 4.3.1 92 Independence............................................................................................................ 4.3 89 Sets...................... about Reasoning by Probabilities Computing 4.2.3 87 Events...................................................................... of Probability The 4.2.2 83 Outcomes............. Counting by Probabilities Event Computing 4.2.1 81 Events........................................................................................................................... 4.2 79 Probability...................................................................... and Outcomes 4.1.1 79 Probability....................................................... and Outcomes Experiments, 4.1 79 probability in ideas Basic 4 78  , Probability II 72 . . . . . . . . . . . . . . . . . 72 . . . . . . . . . . . . . . . . . 72 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . to: able be 3.4.5 . . . . . . procedures: these use 3.4.4 . . . . . facts: these remember 3.4.3 72 terms............................................................................ these remember 3.4.2 72 definitions:................................................................. these remember 3.4.1 72 should.................................................................................................................. You 3.4 1 68 correlation......................................................... by caused Confusion 3.2.3 64 Predict................................................................ to Correlation Using 3.2.2 60 Coefficient................................................................... Correlation The 3.2.1 57 Correlation.................................................................................................................. 3.2 54 Plots..................................... Scatter with Relationships Exposing 3.1.4 53 Data.............................................................. Spatial for Plots Scatter 3.1.3 51 Series.............................................................................................................. 3.1.2 3.1.1 47 Data...................................................................................................... 2D Plotting 3.1 47 Relationships at Looking 3 43 to...................................................................................................... able be 2.6.4 43 facts:............................................................................. these remember 2.6.3 43 terms............................................................................ these remember 2.6.2 43 definitions:................................................................. these remember 2.6.1 43 should.................................................................................................................. You 2.6 39 Pizzas...................................... Australian Investigating bigger? is Whose 2.5 38 Plots....................................................................................................... Box 2.4.3 34 Data......................................... Normal and Coordinates Standard 2.4.2 31 Histograms.......................................................... of Properties Some 2.4.1 31 Summaries............................................................................................. and Plots 2.4 30 Sensibly.................................................................... Summaries Using 2.3.7 29 Range.................................................................................. Interquartile 2.3.6 27 Median.................................................................................................. The 2.3.5 26 Variance......................................................................................................... 2.3.4 26 Online...................... Deviation Standard and Mean Computing 2.3.3 22 Deviation................................................................................... Standard 2.3.2 20 Mean...................................................................................................... The 2.3.1 19 Data............................................................................................ 1D Summarizing 2.3 19 Histograms.......................................................................... Conditional 2.2.4 17 Histograms...................................................................... Make to How 2.2.3 16 Histograms................................................................................................... 2.2.2 Bar 2.2.1 15 Data................................................................. Plotting - Happening? What's 2.2 13 Datasets....................................................................................................................... 2.1 13 Data at Looking for Tools First 2 12 , Datasets Describing I 11 Acknowledgements................................................................................................. 1.1 10 Information........................................................................ Background 1.0.1 9 conventions and Notation 1 Plugins sind zusätzliche Softwareerweiterungen haben Sie den vollen Durchblick und wissen sofort, was gemeint ist Ein responsives Design erlaubt die Anpassung an die unterschiedlichen Bildschirmgrößen die vom Verbraucher heruntergeladen oder online in einem nichtöffentlichen Bereich eingesehen werden können Ein eCommerce Vertrag ist ein Vertrag

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