Book Image

Building Probabilistic Graphical Models with Python

By : Kiran R Karkera
Book Image

Building Probabilistic Graphical Models with Python

By: Kiran R Karkera

Overview of this book

<p>With the increasing prominence in machine learning and data science applications, probabilistic graphical models are a new tool that machine learning users can use to discover and analyze structures in complex problems. The variety of tools and algorithms under the PGM framework extend to many domains such as natural language processing, speech processing, image processing, and disease diagnosis.</p> <p>You've probably heard of graphical models before, and you're keen to try out new landscapes in the machine learning area. This book gives you enough background information to get started on graphical models, while keeping the math to a minimum.</p>
Table of Contents (15 chapters)
Building Probabilistic Graphical Models with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

About the Reviewers

Mohit Goenka graduated from the University of Southern California (USC) with a Master's degree in Computer Science. His thesis focused on game theory and human behavior concepts as applied in real-world security games. He also received an award for academic excellence from the Office of International Services at the University of Southern California. He has showcased his presence in various realms of computers including artificial intelligence, machine learning, path planning, multiagent systems, neural networks, computer vision, computer networks, and operating systems.

During his tenure as a student, Mohit won multiple competitions cracking codes and presented his work on Detection of Untouched UFOs to a wide range of audience. Not only is he a software developer by profession, but coding is also his hobby. He spends most of his free time learning about new technology and grooming his skills.

What adds a feather to Mohit's cap is his poetic skills. Some of his works are part of the University of Southern California libraries archived under the cover of the Lewis Carroll Collection. In addition to this, he has made significant contributions by volunteering to serve the community.

Shangpu Jiang is doing his PhD in Computer Science at the University of Oregon. He is interested in machine learning and data mining and has been working in this area for more than six years. He received his Bachelor's and Master's degrees from China.

Jing (Dave) Tian is now a graduate researcher and is doing his PhD in Computer Science at the University of Oregon. He is a member of the OSIRIS lab. His research direction involves system security, embedded system security, trusted computing, and static analysis for security and virtualization. He is interested in Linux kernel hacking and compilers. He also spent a year on AI and machine learning direction and taught the classes Intro to Problem Solving using Python and Operating Systems in the Computer Science department. Before that, he worked as a software developer in the Linux Control Platform (LCP) group at the Alcatel-Lucent (former Lucent Technologies) R&D department for around four years. He got his Bachelor's and Master's degrees from EE in China.

Xiao Xiao is a PhD student studying Computer Science at the University of Oregon. Her research interests lie in machine learning, especially probabilistic graphical models. Her previous project was to compare two inference algorithms' performance on a graphical model (relational dependency network).