Includes bibliographical references and index.
|Statement||Sushmita Mitra, Sujay Datta.|
|Series||Computer science and data analysis series|
|LC Classifications||QH324.2 .M58 2008|
|The Physical Object|
|LC Control Number||2008008649|
Book Description. Lucidly Integrates Current Activities. Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics. Book Abstract: The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate by: Introduction to Machine Learning and Bioinformatics Sushmita Mitra, Sujay Datta, Theodore Perkins, and George Michailidis Chapman & Hall/CRC, Boca Raton, Florida, ISBN pp. USD Introduction Machine learning (Hastie et al. ) is a sub-set of arti cial intelligence and deals with techniques to allow computers.
Get this from a library! Introduction to machine learning and bioinformatics. [Sushmita Mitra;] -- "Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of. One of my favorite books on bioinformatics was written by a friend of mine. It is a thin book with many examples: “Practical Bioinformatics” by Michael Agostino, published by Garland Science. It is available from Amazon. After that, continue with. The one that I preferred after going through the contents of many machine learning books for bioinformatics: Data Mining in Bioinformatics by Jason T.L. Wang, Mohammed J. Zaki, Hannu T. T. Toivonen and Dennis Shasha ~Akash. An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already.
Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject. An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding. Ethem Alpaydin's Introduction to Machine Learning provides a nice blending of the topical coverage of machine learning (à la Tom Mitchell) with formal probabilistic foundations (à la Christopher Bishop). This newly updated version now introduces some of the most recent and important topics in machine learning (e.g., spectral methods, deep learning, and learning to rank) to students and. Full text of "Introduction To Machine Learning And Bioinformatics" See other formats.