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The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them on their way to a career in data science and machine learning.It is for a beginning course in machine learning, data mining & analytics, data science, or general data analysis.
Table des matières
Preface. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. A Case Study in Predicting Treatment Outcome. Bibliography. Appendix A: Supplementary Materials and More Datasets. Appendix B: Statistics for Performance Evaluation. Subject Index. Index of R Functions. Script Index.
A propos de l'auteur
Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for 27 years. Dr. Roiger’s Ph.D. degree is in Computer & Information Sciences from the University of Minnesota. Dr. Roiger continues to serve as a part-time faculty member teaching courses in data mining, artificial intelligence and research methods. Richard enjoys interacting with his grandchildren, traveling, writing and pursuing his musical talents.
Résumé
The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them on their way to a career in data science and machine learning.It is for a beginning course in machine learning, data mining & analytics, data science, or general data analysis.
