Torrent details for "Polyanskiy Y., Yihong W. Information Theory. From Coding to Lear…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
6.5 MB
Info Hash:
D562C065D70746E206B7A4F85A6DC7E86D83B5C8
Added By:
Added:
March 23, 2026, 9:53 a.m.
Stats:
|
(Last updated: March 23, 2026, 1:08 p.m.)
| File | Size |
|---|---|
| Polyanskiy Y., Yihong W. Information Theory. From Coding to Learning 2025.pdf | 6.5 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Polyanskiy Y., Yihong W. Information Theory. From Coding to Learning 2025
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format
This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning, equipping students with a uniquely well-rounded and rigorous foundation for further study. Introduces core topics such as data compression, channel coding, and rate-distortion theory using a unique finite block-length approach. With over 210 end-of-part exercises and numerous examples, students are introduced to contemporary applications in statistics, machine learning and modern communication theory. This textbook presents information-theoretic methods with applications in statistical learning and computer science, such as f-divergences, PAC Bayes and variational principle, Kolmogorov's metric entropy, strong data processing inequalities, and entropic upper bounds for statistical estimation. Accompanied by a solutions manual for instructors, and additional standalone chapters on more specialized topics in information theory, this is the ideal introductory textbook for senior undergraduate and graduate students in electrical engineering, statistics, and computer science
×