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Machine Learning with R [[electronic resource] /] / by Abhijit Ghatak
| Machine Learning with R [[electronic resource] /] / by Abhijit Ghatak |
| Autore | Ghatak Abhijit |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIX, 210 p. 56 illus.) |
| Disciplina | 519.502855133 |
| Soggetto topico |
Artificial intelligence
Computer programming Programming languages (Electronic computers) Database management R (Computer program language) Artificial Intelligence Programming Techniques Programming Languages, Compilers, Interpreters Database Management |
| ISBN | 981-10-6808-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- The Data-Driven Universe -- Causality-The Cornerstone of Accountability -- The Growth of Machines -- What is Machine Learning? -- Intended Audience -- Acknowledgements -- Contents -- About the Author -- 1 Linear Algebra, Numerical Optimization, and Its Applications in Machine Learning -- 1.1 Scalars, Vectors, and Linear Functions -- 1.1.1 Scalars -- 1.1.2 Vectors -- 1.2 Linear Functions -- 1.3 Matrices -- 1.3.1 Transpose of a Matrix -- 1.3.2 Identity Matrix -- 1.3.3 Inverse of a Matrix -- 1.3.4 Representing Linear Equations in Matrix Form -- 1.4 Matrix Transformations -- 1.5 Norms -- 1.5.1 ell2 Optimization -- 1.5.2 ell1 Optimization -- 1.6 Rewriting the Regression Model in Matrix Notation -- 1.7 Cost of a n-Dimensional Function -- 1.8 Computing the Gradient of the Cost -- 1.8.1 Closed-Form Solution -- 1.8.2 Gradient Descent -- 1.9 An Example of Gradient Descent Optimization -- 1.10 Eigendecomposition -- 1.11 Singular Value Decomposition (SVD) -- 1.12 Principal Component Analysis (PCA) -- 1.12.1 PCA and SVD -- 1.13 Computational Errors -- 1.13.1 Rounding---Overflow and Underflow -- 1.13.2 Conditioning -- 1.14 Numerical Optimization -- 2 Probability and Distributions -- 2.1 Sources of Uncertainty -- 2.2 Random Experiment -- 2.3 Probability -- 2.3.1 Marginal Probability -- 2.3.2 Conditional Probability -- 2.3.3 The Chain Rule -- 2.4 Bayes' Rule -- 2.5 Probability Distribution -- 2.5.1 Discrete Probability Distribution -- 2.5.2 Continuous Probability Distribution -- 2.5.3 Cumulative Probability Distribution -- 2.5.4 Joint Probability Distribution -- 2.6 Measures of Central Tendency -- 2.7 Dispersion -- 2.8 Covariance and Correlation -- 2.9 Shape of a Distribution -- 2.10 Chebyshev's Inequality -- 2.11 Common Probability Distributions -- 2.11.1 Discrete Distributions -- 2.11.2 Continuous Distributions.
2.11.3 Summary of Probability Distributions -- 2.12 Tests for Fit -- 2.12.1 Chi-Square Distribution -- 2.12.2 Chi-Square Test -- 2.13 Ratio Distributions -- 2.13.1 Student's t-Distribution -- 2.13.2 F-Distribution -- 3 Introduction to Machine Learning -- 3.1 Scientific Enquiry -- 3.1.1 Empirical Science -- 3.1.2 Theoretical Science -- 3.1.3 Computational Science -- 3.1.4 e-Science -- 3.2 Machine Learning -- 3.2.1 A Learning Task -- 3.2.2 The Performance Measure -- 3.2.3 The Experience -- 3.3 Train and Test Data -- 3.3.1 Training Error, Generalization (True) Error, and Test Error -- 3.4 Irreducible Error, Bias, and Variance -- 3.5 Bias--Variance Trade-off -- 3.6 Deriving the Expected Prediction Error -- 3.7 Underfitting and Overfitting -- 3.8 Regularization -- 3.9 Hyperparameters -- 3.10 Cross-Validation -- 3.11 Maximum Likelihood Estimation -- 3.12 Gradient Descent -- 3.13 Building a Machine Learning Algorithm -- 3.13.1 Challenges in Learning Algorithms -- 3.13.2 Curse of Dimensionality and Feature Engineering -- 3.14 Conclusion -- 4 Regression -- 4.1 Linear Regression -- 4.1.1 Hypothesis Function -- 4.1.2 Cost Function -- 4.2 Linear Regression as Ordinary Least Squares -- 4.3 Linear Regression as Maximum Likelihood -- 4.4 Gradient Descent -- 4.4.1 Gradient of RSS -- 4.4.2 Closed Form Solution -- 4.4.3 Step-by-Step Batch Gradient Descent -- 4.4.4 Writing the Batch Gradient Descent Application -- 4.4.5 Writing the Stochastic Gradient Descent Application -- 4.5 Linear Regression Assumptions -- 4.6 Summary of Regression Outputs -- 4.7 Ridge Regression -- 4.7.1 Computing the Gradient of Ridge Regression -- 4.7.2 Writing the Ridge Regression Gradient Descent Application -- 4.8 Assessing Performance -- 4.8.1 Sources of Error Revisited -- 4.8.2 Bias--Variance Trade-Off in Ridge Regression -- 4.9 Lasso Regression. 4.9.1 Coordinate Descent for Least Squares Regression -- 4.9.2 Coordinate Descent for Lasso -- 4.9.3 Writing the Lasso Coordinate Descent Application -- 4.9.4 Implementing Coordinate Descent -- 4.9.5 Bias Variance Trade-Off in Lasso Regression -- 5 Classification -- 5.1 Linear Classifiers -- 5.1.1 Linear Classifier Model -- 5.1.2 Interpreting the Score -- 5.2 Logistic Regression -- 5.2.1 Likelihood Function -- 5.2.2 Model Selection with Log-Likelihood -- 5.2.3 Gradient Ascent to Find the Best Linear Classifier -- 5.2.4 Deriving the Log-Likelihood Function -- 5.2.5 Deriving the Gradient of Log-Likelihood -- 5.2.6 Gradient Ascent for Logistic Regression -- 5.2.7 Writing the Logistic Regression Application -- 5.2.8 A Comparison Using the BFGS Optimization Method -- 5.2.9 Regularization -- 5.2.10 \ell_2 Regularized Logistic Regression -- 5.2.11 \ell_2 Regularized Logistic Regression with Gradient Ascent -- 5.2.12 Writing the Ridge Logistic Regression with Gradient Ascent Application -- 5.2.13 Writing the Lasso Regularized Logistic Regression With Gradient Ascent Application -- 5.3 Decision Trees -- 5.3.1 Decision Tree Algorithm -- 5.3.2 Overfitting in Decision Trees -- 5.3.3 Control of Tree Parameters -- 5.3.4 Writing the Decision Tree Application -- 5.3.5 Unbalanced Data -- 5.4 Assessing Performance -- 5.4.1 Assessing Performance--Logistic Regression -- 5.5 Boosting -- 5.5.1 AdaBoost Learning Ensemble -- 5.5.2 AdaBoost: Learning from Weighted Data -- 5.5.3 AdaBoost: Updating the Weights -- 5.5.4 AdaBoost Algorithm -- 5.5.5 Writing the Weighted Decision Tree Algorithm -- 5.5.6 Writing the AdaBoost Application -- 5.5.7 Performance of our AdaBoost Algorithm -- 5.6 Other Variants -- 5.6.1 Bagging -- 5.6.2 Gradient Boosting -- 5.6.3 XGBoost -- 6 Clustering -- 6.1 The Clustering Algorithm -- 6.2 Clustering Algorithm as Coordinate Descent optimization. 6.3 An Introduction to Text mining -- 6.3.1 Text Mining Application---Reading Multiple Text Files from Multiple Directories -- 6.3.2 Text Mining Application---Creating a Weighted tf-idf Document-Term Matrix -- 6.3.3 Text Mining Application---Exploratory Analysis -- 6.4 Writing the Clustering Application -- 6.4.1 Smart Initialization of k-means -- 6.4.2 Writing the k-means++ Application -- 6.4.3 Finding the Optimal Number of Centroids -- 6.5 Topic Modeling -- 6.5.1 Clustering and Topic Modeling -- 6.5.2 Latent Dirichlet Allocation for Topic Modeling -- Appendix References and Further Reading. |
| Record Nr. | UNINA-9910254832103321 |
Ghatak Abhijit
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning with r quick start guide : a beginner's guide to implementing machine learning techniques from scratch using r 3. 5 / / Iván Pastor Sanz
| Machine learning with r quick start guide : a beginner's guide to implementing machine learning techniques from scratch using r 3. 5 / / Iván Pastor Sanz |
| Autore | Sanz Iván Pastor |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 |
| Descrizione fisica | 1 online resource (241 pages) : illustrations |
| Disciplina | 519.502855133 |
| Soggetto topico |
R (Computer program language)
Machine learning |
| ISBN | 1-83864-705-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910793442903321 |
|
Sanz Iván Pastor
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| Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning with r quick start guide : a beginner's guide to implementing machine learning techniques from scratch using r 3. 5 / / Iván Pastor Sanz
| Machine learning with r quick start guide : a beginner's guide to implementing machine learning techniques from scratch using r 3. 5 / / Iván Pastor Sanz |
| Autore | Sanz Iván Pastor |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 |
| Descrizione fisica | 1 online resource (241 pages) : illustrations |
| Disciplina | 519.502855133 |
| Soggetto topico |
R (Computer program language)
Machine learning |
| ISBN | 1-83864-705-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910816579603321 |
|
Sanz Iván Pastor
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| Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematics and R programming for machine learning : from the ground up / / William B. Claster
| Mathematics and R programming for machine learning : from the ground up / / William B. Claster |
| Autore | Claster William B. |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, , 2020 |
| Descrizione fisica | 1 online resource (431 pages) |
| Disciplina | 519.502855133 |
| Soggetto topico | R (Computer program language) |
| ISBN |
1-00-305122-7
1-000-19699-2 1-003-05122-7 1-000-19697-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Naïve Rule. Chapter 6. Complete Bayes. Chapter 7. Naïve Bayes Classifier. Chapter 8. Stored Model for Naive Bayes Classifier. Chapter 9. Review of Mathematics for Neural Networks. Chapter 10. Calculus. Chapter 11. Neural Networks -- Feed Forward Process and Back Propagation Process. Chapter 12. Programming a Neural Network using OOP in R. Chapter 13. Adding in a Bias Term. Chapter 14. Modular Version of Neural Networks for Deep Learning. Chapter 15. Deep Learning with Convolutional Neural Networks. Chapter 16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes. |
| Record Nr. | UNINA-9910794321703321 |
|
Claster William B.
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| Boca Raton, FL : , : CRC Press, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematics and R programming for machine learning : from the ground up / / William B. Claster
| Mathematics and R programming for machine learning : from the ground up / / William B. Claster |
| Autore | Claster William B. |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, , 2020 |
| Descrizione fisica | 1 online resource (431 pages) |
| Disciplina | 519.502855133 |
| Soggetto topico | R (Computer program language) |
| ISBN |
1-00-305122-7
1-000-19699-2 1-003-05122-7 1-000-19697-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Naïve Rule. Chapter 6. Complete Bayes. Chapter 7. Naïve Bayes Classifier. Chapter 8. Stored Model for Naive Bayes Classifier. Chapter 9. Review of Mathematics for Neural Networks. Chapter 10. Calculus. Chapter 11. Neural Networks -- Feed Forward Process and Back Propagation Process. Chapter 12. Programming a Neural Network using OOP in R. Chapter 13. Adding in a Bias Term. Chapter 14. Modular Version of Neural Networks for Deep Learning. Chapter 15. Deep Learning with Convolutional Neural Networks. Chapter 16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes. |
| Record Nr. | UNINA-9910818872303321 |
|
Claster William B.
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| Boca Raton, FL : , : CRC Press, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern optimization with R / / Paulo Cortez
| Modern optimization with R / / Paulo Cortez |
| Autore | Cortez Paulo |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xvii, 254 pages) : illustrations |
| Disciplina | 519.502855133 |
| Collana | Use R! |
| Soggetto topico |
R (Computer program language)
Electronic data processing R (Llenguatge de programació) Processament de dades Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
3-030-72819-6
9783030728199 3030728196 9783030728182 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466411303316 |
|
Cortez Paulo
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||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modern optimization with R / / Paulo Cortez
| Modern optimization with R / / Paulo Cortez |
| Autore | Cortez Paulo |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xvii, 254 pages) : illustrations |
| Disciplina | 519.502855133 |
| Collana | Use R! |
| Soggetto topico |
R (Computer program language)
Electronic data processing R (Llenguatge de programació) Processament de dades Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
3-030-72819-6
9783030728199 3030728196 9783030728182 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495228603321 |
|
Cortez Paulo
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern Optimization with R [[electronic resource] /] / by Paulo Cortez
| Modern Optimization with R [[electronic resource] /] / by Paulo Cortez |
| Autore | Cortez Paulo |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (182 p.) |
| Disciplina | 519.502855133 |
| Collana | Use R! |
| Soggetto topico |
Mathematical optimization
R (Computer program language) Optimization Continuous Optimization Discrete Optimization |
| ISBN | 3-319-08263-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. R Basics -- 3. Blind Search -- 4. Local Search -- 5. Population-Based Search -- 6. Multi-Objective Optimization -- 7. Applications. |
| Record Nr. | UNINA-9910299984903321 |
|
Cortez Paulo
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Political Analysis Using R [[electronic resource] /] / by James E. Monogan III
| Political Analysis Using R [[electronic resource] /] / by James E. Monogan III |
| Autore | Monogan III James E |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XIII, 242 p. 38 illus., 19 illus. in color.) |
| Disciplina | 519.502855133 |
| Collana | Use R! |
| Soggetto topico |
Statistics
Political science Social sciences Public administration R (Computer program language) Statistics for Social Sciences, Humanities, Law Political Science Methodology of the Social Sciences Public Administration |
| ISBN | 3-319-23446-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Obtaining R and Downloading Packages -- Loading and Manipulating Data -- Visualizing Data -- Descriptive Statistics -- Basic Inferences -- Linear Models and Regression -- Diagnostics -- Generalized Linear Models -- Using Libraries to Apply Advanced Models -- Time Series Analysis -- Linear Algebra with Programming Applications -- Additional Programming Tools. |
| Record Nr. | UNINA-9910300247203321 |
|
Monogan III James E
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical predictive analytics : back to the future with R, Spark, and more! / / Ralph Winters
| Practical predictive analytics : back to the future with R, Spark, and more! / / Ralph Winters |
| Autore | Winters Ralph |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, [England] ; ; Mumbai, [India] : , : Packt, , 2017 |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Disciplina | 519.502855133 |
| Soggetto topico | R (Computer program language) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781785880469
1785880462 9781785886188 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910493188403321 |
|
Winters Ralph
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| Birmingham, [England] ; ; Mumbai, [India] : , : Packt, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
