Introduction to Machine Learning with Applications in Information Security

[Mark Stamp] ☆ Introduction to Machine Learning with Applications in Information Security ¶ Download Online eBook or Kindle ePUB. Introduction to Machine Learning with Applications in Information Security Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathemat

Author	:	Mark Stamp
Rating	:	4.13 (636 Votes)
Asin	:	1138626783
Format Type	:	paperback
Number of Pages	:	364 Pages
Publish Date	:	2017-09-01
Language	:	English

DESCRIPTION:

About the AuthorMark Stamp is a Professor at San Jose State University, and the author of two textbooks, Information Security: Principles and Practice and Applied Cryptanalysis: Breaking Ciphers in the Real World.

Mark Stamp is a Professor at San Jose State University, and the author of two textbooks, Information Security: Principles and Practice and Applied Cryptanalysis: Breaking Ciphers in the Real World.

Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. For the reader’s benefit, the figures in the book are also available in electronic form, and in color.About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. He received his Ph.D. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: cssu/stamp/ML/. However, anyone with a modest amount of programming experience should have no troub