Introduction to Predictive Learning
This textbook offers a non-mathematical approach to predictive learning, emphasizing methodology and principles. It describes conceptual and philosophical aspects of predictive learning, exploring constructive learning algorithms in a coherent framework.
The book includes:
concepts, such as complexity control, generalization, and basic modeling approaches;philosophical principles of statistical estimation and machine learning;a presentation of statistical learning theory, a framework for learning algorithms;data-analytic methods; neural network and machine learning methodsnon-standard learning methodologies and their SVM-like mathematical description.This book provides a solid methodologies and practical applications for students and practitioners alike. Exercises range from trivial programming to open-ended research questions. Supplemental material includes a solutions manual, lecture slides, data sets, software implementation, and MATLAB scripts.
Offers a unifying/coherent treatment of learning from data in terms of fundamental underlying concepts.Presents a mixture of mathematical and philosophical concepts related to predictive learning and inductionExplains technical aspects of learning methods by means of examples and application studiesProvides problems, programming assignments and writing assignments at the end of each chapter.