Learning Bayesian Networks

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of ...

Troubleshooting and Maintaining Cisco IP Networks (TSHOOT) Foundation Learning Guide is a Cisco authorized learning tool for CCNP preparation. As part of the Cisco Press foundation learning series, this book covers how to maintain and monitor complex enterprise networks. The chapters focus on planning tasks, evaluations of designs, performance measurements, configuring and verifying, and correct troubleshooting procedures and documentation tasks. From this book you will learn the foundational topics for critical analysis, planning, verification and documentation, while configuring tasks would have been mastered in the CCNP ROUTE and CCNP SWITCH material. The author walks you through several real-world troubleshooting examples to help you refine you...

Join author John Zdziarski for a look inside the brilliant minds that have conceived clever new ways to fight spam in all its nefarious forms. This landmark title describes, in-depth, how statistical filtering is being used by next-generation spam filters to identify and filter unwanted messages, how spam filtering works and how language classification and machine learning combine to produce remarkably accurate spam filters.After reading Ending Spam, you'll have a complete understanding of the mathematical approaches used by today's spam filters as well as decoding, tokenization, various algorithms (including Bayesian analysis and Markovian discrimination) and the benefits of using open-source solutions to end spam. Zdziarski interviewed creators of ...