Coursera - Probabilistic Graphical Models (Stanford University)

[aretr]

Coursera - Probabilistic Graphical Models (Stanford University)
WEBRip | English | MP4 + PDF Slides | 960 x 540 | AVC ~39.6 kbps | 15 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 23:25:47 | 1.36 GB
Genre: eLearning Video / Computer Science, Engineering and Technology

This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables.

These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations.
In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems.
DOWNLOAD

Code:

http://nitroflare.com/view/DA67CDEA96A4B05/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part1.rar
http://nitroflare.com/view/FF0F8A33AEA6D88/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part2.rar
http://nitroflare.com/view/CD6BEA55775659F/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part3.rar
http://nitroflare.com/view/41B3088690F44C0/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part4.rar
http://nitroflare.com/view/DE48AAE90AC5C67/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part5.rar
http://nitroflare.com/view/34BE8648CB0FD06/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part6.rar
http://nitroflare.com/view/501BEF61044534D/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part7.rar
http://nitroflare.com/view/41BBE79CD52A625/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part8.rar

Code:

https://rapidgator.net/file/ed3f621ed014f53a42035766c3db1b76//1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part1.rar
https://rapidgator.net/file/061e3a82aae3cef55fb78f1ebe2a1717/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part2.rar
https://rapidgator.net/file/c0fc6d9e25fec9e76f911b711785c398/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part3.rar
https://rapidgator.net/file/16a4ccf03e2b665826932143d7920f42/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part4.rar
https://rapidgator.net/file/1f02303a9aa08a3bfb883313d24a7e75/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part5.rar
https://rapidgator.net/file/f7b056c567cd82bac9c11bff35b849d0/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part6.rar
https://rapidgator.net/file/b392e765b02f8e65ba476c4d80ff607f/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part7.rar
https://rapidgator.net/file/155000717048bc68a352f4156966918f/1crvb.Coursera..Probabilistic.Graphical.Models.Stanford.University.part8.rar