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After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are available online.
The intended audience is PhD students, researchers and practitioners from various disciplines, including economics and other social sciences, medicine and epidemiology, who have a good understanding of basic statistics and a working knowledge of statistical software, and who want to apply machine learning methods in their work.
After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are available online.
The intended audience is PhD students, researchers and practitioners from various disciplines, including economics and other social sciences, medicine and epidemiology, who have a good understanding of basic statistics and a working knowledge of statistical software, and who want to apply machine learning methods in their work.
Dr. Giovanni Cerulli is a Senior Researcher at the CNR-IRCrES, Research Institute on Sustainable Economic Growth, National Research Council of Italy in Rome. His research interests are in applied econometrics, with a special focus on causal inference and machine learning. He has developed original causal inference models, such as dose-response and treatment models with social interaction, and has carried out many Stata commands for causal inference and machine learning. He has published articles in several high-quality scientific journals, and a book: Econometric Evaluation of Socio-Economic Programs: Theory and Applications. He is currently the Editor-in-Chief of The International Journal of Computational Economics and Econometrics.
Presents the fundamental notions of supervised machine learning
Provides a balance between the theory and applications of machine learning using Python, R, and Stata
Fosters an understanding and awareness of machine learning methods over different software platforms
| Erscheinungsjahr: | 2023 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Reihe: | Statistics and Computing |
| Inhalt: |
xxix
391 S. 45 s/w Illustr. 99 farbige Illustr. 391 p. 144 illus. 99 illus. in color. |
| ISBN-13: | 9783031413360 |
| ISBN-10: | 3031413369 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Cerulli, Giovanni |
| Auflage: | 1st ed. 2023 |
| Hersteller: |
Springer International Publishing
Statistics and Computing |
| Maße: | 241 x 160 x 27 mm |
| Von/Mit: | Giovanni Cerulli |
| Erscheinungsdatum: | 15.11.2023 |
| Gewicht: | 0,876 kg |
Dr. Giovanni Cerulli is a Senior Researcher at the CNR-IRCrES, Research Institute on Sustainable Economic Growth, National Research Council of Italy in Rome. His research interests are in applied econometrics, with a special focus on causal inference and machine learning. He has developed original causal inference models, such as dose-response and treatment models with social interaction, and has carried out many Stata commands for causal inference and machine learning. He has published articles in several high-quality scientific journals, and a book: Econometric Evaluation of Socio-Economic Programs: Theory and Applications. He is currently the Editor-in-Chief of The International Journal of Computational Economics and Econometrics.
Presents the fundamental notions of supervised machine learning
Provides a balance between the theory and applications of machine learning using Python, R, and Stata
Fosters an understanding and awareness of machine learning methods over different software platforms
| Erscheinungsjahr: | 2023 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Reihe: | Statistics and Computing |
| Inhalt: |
xxix
391 S. 45 s/w Illustr. 99 farbige Illustr. 391 p. 144 illus. 99 illus. in color. |
| ISBN-13: | 9783031413360 |
| ISBN-10: | 3031413369 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Cerulli, Giovanni |
| Auflage: | 1st ed. 2023 |
| Hersteller: |
Springer International Publishing
Statistics and Computing |
| Maße: | 241 x 160 x 27 mm |
| Von/Mit: | Giovanni Cerulli |
| Erscheinungsdatum: | 15.11.2023 |
| Gewicht: | 0,876 kg |
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