Understanding Machine Learning: From Theory to Algorithms
Book Name: Understanding Machine Learning: From Theory to Algorithms
Author: Shalev-Shwartz S., Ben-David S.
Publisher: Cambridge University Press 1 edition
Pages: 410 pages
File size: 5 MB
File format: PDF
Understanding Machine Learning: From Theory to Algorithms Pdf Book Description:
The term machine learning refers to the automated detection of meaningful patterns in data. In the past couple of decades it has become a common tool in almost any task that requires information extraction from large data sets. We are surrounded by a machine learning based technology: Search engines learn how to bring us the best results (while placing profitable ads), antispam software learns to filter our email messages, and credit card transactions are secured by a software that learns how to detect frauds. Digital cameras learn to detect faces and intelligent personal assistance applications on smart-phones learn to recognize voice commands. Cars are equipped with accident prevention systems that are built using machine learning algorithms.
Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy. One common feature of all of these applications is that, in contrast to more traditional uses of computers, in these cases, due to the complexity of the patterns that need to be detected, a human programmer cannot provide an explicit, fine-detailed specification of how such tasks should be executed. Taking example from intelligent beings, many of our skills are acquired or refined through learning from our experience (rather than following explicit instructions given to us). Machine learning tools are concerned with endowing programs with the ability to “learn” and adapt. The first goal of this book is to provide a rigorous, yet easy to follow, introduction to the main concepts underlying machine learning: What is learning? How can a machine learn? How do we quantify the resources needed to learn a given concept? Is learning always possible? Can we know whether the learning process succeeded or failed?
DMCA Disclaimer: This site complies with DMCA Digital Copyright Laws. Please bear in mind that we do not own copyrights to these books. We’re sharing this material with our audience ONLY for educational purpose. We highly encourage our visitors to purchase original books from the respected publishers. If someone with copyrights wants us to remove this content, please contact us immediately. All books on the edubookpdf.com are free and NOT HOSTED ON OUR WEBSITE. If you feel that we have violated your copyrights, then please contact us immediately (click here).