Carsten Lange is an economics professor at Cal Poly Pomona with a keen interest in making data science and machine learning more accessible. He has authored multiple refereed articles and four books, including his 2004 book on applying neural networks for economics. Carsten is passionate about teaching machine learning and artificial intelligence with a focus on practical applications and hands-on learning.
This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus.
The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various machine learning models.
With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply machine learning algorithms to real-world scenarios.
In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R codeillegalscripts. A companion website further enriches the learning and teaching experience:
1. Introduction 2. Basics of Machine Learning 3. Introduction to R and RStudio 4. k-Nearest Neighbors — Getting Started 5. Linear Regression — Key Machine Learning Concepts 6. Polynomial Regression — Overfitting & Tuning Explained 7. Ridge, Lasso, and Elastic Net — Regularization Explained 8. Logistic Regression — Handling Imbalanced Data 9. Deep Learning — MLP Neural Networks Explained 10. Tree-Based Models — Bootstrapping Explained 11. Interpreting Machine Learning Results 12. Concluding Remarks Index Bibliography
Inhaltsverzeichnis
1. Introduction 2. Basics of Machine Learning 3. Introduction to R and RStudio 4. k-Nearest Neighbors - Getting Started 5. Linear Regression - Key Machine Learning Concepts 6. Polynomial Regression - Overfitting & Tuning Explained 7. Ridge, Lasso, and Elastic Net - Regularization Explained 8. Logistic Regression - Handling Imbalanced Data 9. Deep Learning - MLP Neural Networks Explained 10. Tree-Based Models - Bootstrapping Explained 11. Interpreting Machine Learning Results 12. Concluding Remarks Index Bibliography
