The Basics of Ltem Response Theory Using r

This graduate-level textbook is a tutorial for item response theory that covers both the basics of item response theory and the use of R for preparing graphical presentation in writings about the theory.

Discover the world of Artificial Intelligence (AI) and Machine Learning (ML) through the lens of Microsoft Excel with our comprehensive ebook. Dive deep into the fundamentals of AI, exploring topics such as the difference between Traditional Programming and AI, Abstraction, Action Language, and Adaptive Algorithms.

This undergraduate textbook breaks down the basics of Nuclear Structure and modern Particle Physics. Based on a comprehensive set of course notes, it covers all the introductory material and latest research developments required by third- and fourth-year physics students.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size.