Approximate Inference

Stanford University - Introduction to Artificial Intelligence [repost]  eBooks & eLearning

Posted by ParRus at Nov. 5, 2016
Stanford University - Introduction to Artificial Intelligence [repost]

Stanford University - Introduction to Artificial Intelligence
WEBRip | English | MP4 + PDF Guide | 640 x 360 | AVC ~250 kbps | 30 fps
AAC | 123 Kbps | 44.1 KHz | 2 channels | ~24 hours | 4.42 GB
Genre: eLearning Video / Science, Cybernetics, Probability

Online Introduction to Artificial Intelligence is based on Stanford CS221, Introduction to Artificial Intelligence. This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing. The objective of this class is to teach you modern AI. You learn about the basic techniques and tricks of the trade, at the same level we teach our Stanford students. We also aspire to excite you about the field of AI. Whether you are a seasoned professional, a college student, or a curious high school student - everyone can participate.

Approximate Solutions of Common Fixed-Point Problems  eBooks & eLearning

Posted by Underaglassmoon at July 14, 2016
Approximate Solutions of Common Fixed-Point Problems

Approximate Solutions of Common Fixed-Point Problems
Springer | Mathematics | July 30 2016 | ISBN-10: 3319332538 | 390 pages | pdf | 3.45 mb

Authors: Zaslavski, Alexander J.
Studies the approximate solutions of common fixed point problems and convex feasibility problems in the presence of computational errors
Examines the convergence of component-averaged row projections [CARP]
Extends results for a dynamic string-averaging version of the proximal algorithm

Learning Probabilistic Graphical Models in R  eBooks & eLearning

Posted by Grev27 at May 14, 2016
Learning Probabilistic Graphical Models in R

David Bellot, "Learning Probabilistic Graphical Models in R"
English | ISBN: 1784392057 | 2016 | PDF/EPUB/MOBI | 250 pages | 4 MB/7 MB/11 MB
Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression... (repost)

Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis by Patrick Laurie Davies
English | 2014 | ISBN: 1482215861 | 320 pages | PDF | 11 MB

Mastering Probabilistic Graphical Models using Python  eBooks & eLearning

Posted by AlenMiler at Aug. 13, 2015
Mastering Probabilistic Graphical Models using Python

Mastering Probabilistic Graphical Models using Python by Ankur Ankan
English | 26 July 2015 | ISBN: 1784394688 | 284 Pages | EPUB/MOBI/PDF (True) | 34.35 MB
With: Code Files

If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.
Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression... (repost)

Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis by Patrick Laurie Davies
English | 2014 | ISBN: 1482215861 | 320 pages | PDF | 11 MB
Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression...

Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis by Patrick Laurie Davies
English | 2014 | ISBN: 1482215861 | 320 pages | PDF | 11 MB

"Exercises in Statistical Inference with detailed solutions" by Robert Jonsson  eBooks & eLearning

Posted by exLib at Sept. 2, 2014
"Exercises in Statistical Inference with detailed solutions" by Robert Jonsson

"Exercises in Statistical Inference with detailed solutions" by Robert Jonsson
RoJo, BoBoCoAe | 2014 | ISBN: 8740307522 9788740307528 | 198 pages | PDF | 10 MB

This volume is dedicated to statistical inference - a process of drawing general conclusions from data in a specific sample. The present book differs from the latter since it focuses on problem solving and only a minimum of the theory needed is presented.
"Dynamic Programming and Bayesian Inference, Concepts and Applications" ed. by Mohammad Saber Fallah Nezhad

"Dynamic Programming and Bayesian Inference, Concepts and Applications" ed. by Mohammad Saber Fallah Nezhad
InTAvE | 2014 | ISBN: 953511364X 9789535113645 | 160 pages | PDF | 10 MB

The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming.
Stanford University - Artificial Intelligence (lectures, homework, questions & answers) (2011)

Stanford University - Artificial Intelligence (lectures, homework, questions & answers) (2011)
eLearning | English | 640x360 | H264 ~250 kbps | MP4A ~128 Kbps | 4.42 GB
Mathematics, Probability

Online Introduction to Artificial Intelligence is based on Stanford CS221, Introduction to Artificial Intelligence. This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing.