An Introduction to Machine Learning / Miroslav Kubat.
By: Kubat, Miroslav
Contributor(s): SpringerLink (Online service)Material type: TextPublisher: Cham : Springer, c.2015Description: xiii, 291 p. : ill. ; 24 cmISBN: 9783319200095Subject(s): Machine learning | Informatics and Computer Science May2016Additional physical formats: Printed edition:: No titleDDC classification: 006.3
|Item type||Current location||Collection||Call number||Status||Date due||Barcode||Item holds|
|Book - Borrowing||Central Library Lower Floor||Alahram||006.3 KUB (Browse shelf)||Available||000032448|
Browsing Central Library Shelves , Shelving location: Lower Floor Close shelf browser
|006.3 KHE A first course in artificial intelligence /||006.3 KHE A first course in artificial intelligence /||006.3 KHE A first course in artificial intelligence /||006.3 KUB An Introduction to Machine Learning /||006.3 LAR The large, the small, and the human mind /||006.3 LUG Artificial intelligence :||006.3 LUG Artificial intelligence :|
Index : p. 291.
Bibliography : p. 287-290.
A Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation.-Statistical Significance -- The Genetic Algorithm -- Reinforcement learning.
Available on campus and off campus with authorized login.
This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting, ” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.