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Algorithmic Regimes : Methods, Interactions, and Politics
| Algorithmic Regimes : Methods, Interactions, and Politics |
| Autore | Jarke Juliane |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam : , : Amsterdam University Press, , 2024 |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 303.4833 |
| Altri autori (Persone) |
PrietlBianca
EgbertSimon BoevaYana HeuerHendrik ArnoldMaike |
| Collana | Digital Studies |
| Soggetto topico | COMPUTERS / Database Management / Data Mining |
| Soggetto non controllato | Algorithmic regimes, datafication, critical data studies, algorithm studies, science and technology studies |
| ISBN | 90-485-5690-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Table of Contents -- 1. Knowing in Algorithmic Regimes: An Introduction -- Juliane Jarke, Bianca Prietl, Simon Egbert, Yana Boeva, and Hendrik Heuer -- I. METHODS -- 2. Revisiting Transparency Efforts in Algorithmic Regimes -- Motahhare Eslami and Hendrik Heuer -- 3. Understanding and Analysing Science's Algorithmic Regimes: A Primer in Computational Science Code Studies -- Gabriele Gramelsberger, Daniel Wenz, and Dawid Kasprowicz -- 4. Sensitizing for Algorithms: Foregrounding Experience in the Interpretive Study of Algorithmic Regimes -- Elias Storms and Oscar Alvarado -- 5. Reassembling the Black Box of Machine Learning: Of Monsters and the Reversibility of Foldings -- Juliane Jarke and Hendrik Heuer -- 6. Commentary: Methods in Algorithmic Regimes -- Adrian Mackenzie -- II. INTERACTIONS -- 7. Buildings in the Algorithmic Regime: Infrastructuring Processes in Computational Design -- Yana Boeva and Cordula Kropp -- 8. The Organization in the Loop: Exploring Organizations as Complex Elements of Algorithmic Assemblages -- Stefanie Büchner, Henrik Dosdall, and Ioanna Constantiou -- 9. Algorithm-Driven Reconfigurations of Trust Regimes: An Analysis of the Potentiality of Fake News -- Jörn Wiengarn and Maike Arnold -- 10. Recommender Systems beyond the Filter Bubble: Algorithmic Media and the Fabrication of Publics -- Nikolaus Poechhacker, Marcus Burkhardt, and Jan-Hendrik Passoth -- 11. Commentary: Taking to Machines: Knowledge Production and Social Relations in the Age of Governance by Data Infrastructure -- Stefania Milan -- III. POLITICS -- 12. The Politics of Data Science: Institutionalizing Algorithmic Regimes of Knowledge Production -- Bianca Prietl and Stefanie Raible -- 13. Algorithmic Futures: Governmentality and Prediction Regimes -- Simon Egbert -- 14. Power and Resistance in the Twitter Bias Discourse -- Paola Lopez.
15. Making Algorithms Fair: Ethnographic Insights from Machine Learning Interventions -- Katharina Kinder-Kurlanda and Miriam Fahimi -- 16. Commentary: The Entanglements, Experiments, and Uncertainties of Algorithmic Regimes -- Nanna Bonde Thylstrup -- Index. |
| Record Nr. | UNISA-996588067403316 |
Jarke Juliane
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| Amsterdam : , : Amsterdam University Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithmic Regimes : Methods, Interactions, and Politics
| Algorithmic Regimes : Methods, Interactions, and Politics |
| Autore | Jarke Juliane |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam : , : Amsterdam University Press, , 2024 |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 303.4833 |
| Altri autori (Persone) |
PrietlBianca
EgbertSimon BoevaYana HeuerHendrik ArnoldMaike |
| Collana | Digital Studies |
| Soggetto topico | COMPUTERS / Database Management / Data Mining |
| Soggetto non controllato | Algorithmic regimes, datafication, critical data studies, algorithm studies, science and technology studies |
| ISBN | 90-485-5690-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Table of Contents -- 1. Knowing in Algorithmic Regimes: An Introduction -- Juliane Jarke, Bianca Prietl, Simon Egbert, Yana Boeva, and Hendrik Heuer -- I. METHODS -- 2. Revisiting Transparency Efforts in Algorithmic Regimes -- Motahhare Eslami and Hendrik Heuer -- 3. Understanding and Analysing Science's Algorithmic Regimes: A Primer in Computational Science Code Studies -- Gabriele Gramelsberger, Daniel Wenz, and Dawid Kasprowicz -- 4. Sensitizing for Algorithms: Foregrounding Experience in the Interpretive Study of Algorithmic Regimes -- Elias Storms and Oscar Alvarado -- 5. Reassembling the Black Box of Machine Learning: Of Monsters and the Reversibility of Foldings -- Juliane Jarke and Hendrik Heuer -- 6. Commentary: Methods in Algorithmic Regimes -- Adrian Mackenzie -- II. INTERACTIONS -- 7. Buildings in the Algorithmic Regime: Infrastructuring Processes in Computational Design -- Yana Boeva and Cordula Kropp -- 8. The Organization in the Loop: Exploring Organizations as Complex Elements of Algorithmic Assemblages -- Stefanie Büchner, Henrik Dosdall, and Ioanna Constantiou -- 9. Algorithm-Driven Reconfigurations of Trust Regimes: An Analysis of the Potentiality of Fake News -- Jörn Wiengarn and Maike Arnold -- 10. Recommender Systems beyond the Filter Bubble: Algorithmic Media and the Fabrication of Publics -- Nikolaus Poechhacker, Marcus Burkhardt, and Jan-Hendrik Passoth -- 11. Commentary: Taking to Machines: Knowledge Production and Social Relations in the Age of Governance by Data Infrastructure -- Stefania Milan -- III. POLITICS -- 12. The Politics of Data Science: Institutionalizing Algorithmic Regimes of Knowledge Production -- Bianca Prietl and Stefanie Raible -- 13. Algorithmic Futures: Governmentality and Prediction Regimes -- Simon Egbert -- 14. Power and Resistance in the Twitter Bias Discourse -- Paola Lopez.
15. Making Algorithms Fair: Ethnographic Insights from Machine Learning Interventions -- Katharina Kinder-Kurlanda and Miriam Fahimi -- 16. Commentary: The Entanglements, Experiments, and Uncertainties of Algorithmic Regimes -- Nanna Bonde Thylstrup -- Index. |
| Record Nr. | UNINA-9910806000303321 |
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Jarke Juliane
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| Amsterdam : , : Amsterdam University Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Anthology in Digital Culture : Forms and Affordances
| The Anthology in Digital Culture : Forms and Affordances |
| Autore | Taurino Giulia |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam : , : Amsterdam University Press, , 2024 |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 302.231 |
| Soggetto topico |
Anthologies - History
COMPUTERS / Database Management / Data Mining |
| Soggetto non controllato | anthology, streaming platforms, recommendation systems, algorithmic culture |
| ISBN | 9789048554591 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996580166403316 |
|
Taurino Giulia
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| Amsterdam : , : Amsterdam University Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
The Anthology in Digital Culture : Forms and Affordances
| The Anthology in Digital Culture : Forms and Affordances |
| Autore | Taurino Giulia |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam : , : Amsterdam University Press, , 2024 |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 302.231 |
| Soggetto topico |
Anthologies - History
COMPUTERS / Database Management / Data Mining |
| ISBN | 9789048554591 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Table of Contents -- Preface -- Introduction -- 1. History -- 2. Design -- 3. Infrastructures -- 4. Platforms -- Conclusion -- Appendix -- Bibliography -- Index |
| Record Nr. | UNINA-9910766879303321 |
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Taurino Giulia
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| Amsterdam : , : Amsterdam University Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big data, data mining, and machine learning : value creation for business leaders and practitioners
| Big data, data mining, and machine learning : value creation for business leaders and practitioners |
| Autore | Dean Jared |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (289 pages) |
| Disciplina |
658
658.05631 658/.05631 |
| Collana |
Wiley and SAS business series
THEi Wiley ebooks |
| Soggetto topico |
Big data
COMPUTERS / Database Management / Data Mining Data mining Database management Information technology -- Management Management -- Data processing Management |
| ISBN | 1-118-69178-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Big Data, Data Mining, and Machine Learning; Contents; Forward; Preface; Acknowledgments; Introduction; Big Data Timeline; Why This Topic Is Relevant Now; Is Big Data a Fad?; Where Using Big Data Makes a Big Difference; Technical Issue; Work Flow Productivity; The Complexities When Data Gets Large; Part One The Computing Environment; Chapter 1 Hardware; Storage (Disk); Central Processing Unit; Graphical Processing Unit; Memory; Network; Chapter 2 Distributed Systems; Database Computing; File System Computing; Considerations; Chapter 3 Analytical Tools; Weka; Java and JVM Languages; R; Python
SASPart Two Turning Data into Business Value; Chapter 4 Predictive Modeling; A Methodology for Building Models; sEMMA; sEMMA for the Big Data Era; Binary Classification; Multilevel Classification; Interval Prediction; Assessment of Predictive Models; Classification; Receiver Operating Characteristic; Lift; Gain; Akaike's Information Criterion; Bayesian Information Criterion; Kolmogorov‐Smirnov; Chapter 5 Common Predictive Modeling Techniques; RFM; Regression; Basic Example of Ordinary Least Squares; Assumptions of Regression Models; Additional Regression Techniques Applications in the Big Data EraGeneralized Linear Models; Example of a Probit GLM; Applications in the Big Data Era; Neural Networks; Basic Example of Neural Networks; Decision and Regression Trees; Support Vector Machines; Bayesian Methods Network Classification; Naive Bayes Network; Parameter Learning; Learning a Bayesian Network; Inference in Bayesian Networks; Scoring for Supervised Learning; Ensemble Methods; Chapter 6 Segmentation; Cluster Analysis; Distance Measures (Metrics); Evaluating Clustering; Number of Clusters; K-means Algorithm; Hierarchical Clustering; Profiling Clusters Chapter 7 Incremental Response ModelingBuilding the Response Model; Measuring the Incremental Response; Chapter 8 Time Series Data Mining; Reducing Dimensionality; Detecting Patterns; Fraud Detection; New Product Forecasting; Time Series Data Mining in Action: Nike+ FuelBand; Seasonal Analysis; Trend Analysis; Similarity Analysis; Chapter 9 Recommendation Systems; What Are Recommendation Systems?; Where Are They Used?; How Do They Work?; Baseline Model; Low‐Rank Matrix Factorization; Stochastic Gradient Descent; Alternating Least Squares; Restricted Boltzmann Machines; Contrastive Divergence Assessing Recommendation QualityRecommendations in Action: SAS Library; Chapter 10 Text Analytics; Information Retrieval; Content Categorization; Text Mining; Text Analytics in Action: Let's Play Jeopardy!; Information Retrieval Steps; Discovering Topics in Jeopardy! Clues; Topics from Clues Having Incorrect or Missing Answers; Discovering New Topics from Clues; Contestant Analysis: Fantasy Jeopardy!; Part Three Success Stories of Putting It All Together; Chapter 11 Case Study of a Large U.S.-Based Financial Services Company; Traditional Marketing Campaign Process High-Performance Marketing Solution |
| Record Nr. | UNINA-9910132334903321 |
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Dean Jared
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| Hoboken : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Managing Datasets and Models
| Managing Datasets and Models |
| Autore | Campesato Oswald |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2023 |
| Descrizione fisica | 1 online resource (387 pages) |
| Disciplina | 005.133 |
| Soggetto topico |
Python (Computer program language)
COMPUTERS / Database Management / Data Mining |
| Soggetto non controllato |
Data Mining
Computers |
| ISBN |
1-68392-950-0
1-68392-951-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover -- Half-Title Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Chapter 1: Working with Data -- Import Statements for this Chapter -- Exploratory Data Analysis (EDA) -- Dealing with Data: What Can Go Wrong? -- Analyzing Missing Data -- Explanation of Data Types -- Data Preprocessing -- Working with Data Types -- What is Drift? -- What is Data Leakage? -- Model Selection and Preparing Datasets -- Types of Dependencies Among Features -- Data Cleaning and Imputation -- Summary -- Chapter 2: Outlier and Anomaly Detection -- Import Statements for this Chapter -- Working with Outliers -- Finding Outliers with NumPy -- Finding Outliers with Pandas -- Finding Outliers with Scikit-Learn (Optional) -- Fraud Detection -- Techniques for Anomaly Detection -- Working with Imbalanced Datasets -- Summary -- Reference -- Chapter 3: Cleaning Datasets -- Prerequisites for this Chapter -- Analyzing Missing Data -- Pandas, CSV Files, and Missing Data -- Missing Data and Imputation -- Skewed Datasets -- CSV Files with Multi-Row Records -- Column Subset and Row Subrange of Titanic CSV File -- Data Normalization -- Handling Categorical Data -- Working with Currency -- Working with Dates -- Working with Quoted Fields -- What is SMOTE? -- Data Wrangling -- Summary -- Chapter 4: Working with Models -- Import Statements for this Chapter -- Techniques for Scaling Data -- Examples of Splitting and Scaling Data -- The Confusion Matrix -- The ROC Curve and AUC Curve -- Exploring the Titanic Dataset -- Steps for Training Classifiers -- Diagram for Partitioned Datasets -- A KNN-Based Model with the wine.csv Dataset -- Other Models with the wine.csv Dataset -- A KNN-Based Model with the bmi.csv Dataset -- A KNN-Based Model with the Diabetes.csv Dataset -- SMOTE and the Titanic Dataset -- EDA and Data Visualization.
What about Regression and Clustering? -- Feature Importance -- What is Feature Engineering? -- What is Feature Selection? -- What is Feature Extraction? -- Data Cleaning and Machine Learning -- Summary -- Chapter 5: Matplotlib and Seaborn -- Import Statements for this Chapter -- What is Data Visualization? -- What is Matplotlib? -- Matplotlib Styles -- Display Attribute Values -- Color Values in Matplotlib -- Cubed Numbers in Matplotlib -- Horizontal Lines in Matplotlib -- Slanted Lines in Matplotlib -- Parallel Slanted Lines in Matplotlib -- Lines and Labeled Vertices in Matplotlib -- A Dotted Grid in Matplotlib -- Lines in a Grid in Matplotlib -- Two Lines and a Legend in Matplotlib -- Loading Images in Matplotlib -- A Checkerboard in Matplotlib -- Randomized Data Points in Matplotlib -- A Set of Line Segments in Matplotlib -- Plotting Multiple Lines in Matplotlib -- Trigonometric Functions in Matplotlib -- A Histogram in Matplotlib -- Histogram with Data from a Sqlite3 Table -- Plot a Best-Fitting Line with ggplot -- Plot Bar Charts -- Plot a Pie Chart -- Heat Maps -- Save Plot as a PNG File -- Working with SweetViz -- Working with Skimpy -- 3D Charts in Matplotlib -- Plotting Financial Data with Mplfinance -- Charts and Graphs with Data from Sqlite3 -- Working with Seaborn -- Seaborn Dataset Names -- Seaborn Built-In Datasets -- The Iris Dataset in Seaborn -- The Titanic Dataset in Seaborn -- Extracting Data from Titanic Dataset in Seaborn (1) -- Extracting Data from Titanic Dataset in Seaborn (2) -- Visualizing a Pandas Data Frame in Seaborn -- Seaborn Heat Maps -- Seaborn Pair Plots -- What is Bokeh? -- Introduction to Scikit-Learn -- The Digits Dataset in Scikit-Learn -- The Iris Dataset in Scikit-Learn (1) -- The Iris Dataset in Scikit-Learn (2) -- Advanced Topics in Seaborn -- Summary -- Appendix: Working with awk -- The awk Command. Aligning Text with the printf() Statement -- Conditional Logic and Control Statements -- Deleting Alternate Lines in Datasets -- Merging Lines in Datasets -- Matching with Metacharacters and Character Sets -- Printing Lines Using Conditional Logic -- Splitting File Names with awk -- Working with Postfix Arithmetic Operators -- Numeric Functions in awk -- One-Line awk Commands -- Useful Short awk Scripts -- Printing the Words in a Text String in awk -- Count Occurrences of a String in Specific Rows -- Printing a String in a Fixed Number of Columns -- Printing a Dataset in a Fixed Number of Columns -- Aligning Columns in Datasets -- Aligning Columns and Multiple Rows in Datasets -- Removing a Column from a Text File -- Subsets of Column-Aligned Rows in Datasets -- Counting Word Frequency in Datasets -- Displaying Only "Pure" Words in a Dataset -- Working with Multi-Line Records in awk -- A Simple Use Case -- Another Use Case -- Summary -- Index. |
| Record Nr. | UNINA-9910838326303321 |
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Campesato Oswald
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| Bloomfield : , : Mercury Learning & Information, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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