Entropy & coding
Discussion of worst-case entropy, Shannon entropy, zero order empirical entropy, unambiguous codes, prefix codes and Huffman coding. ── Course & channel links ── Course playlist: Burrows-Wheeler Indexing • Burrows-Wheeler Indexing ── About the author ── Ben Langmead is a Professor of Computer Science at Johns Hopkins University, where his research spans bioinformatics, computational biology, and data-intensive science. He is the author of Bowtie and Bowtie 2; his group has also developed software like Kraken 2 and resources like recount3 and Index Zone, as well as methods for pangenome indexing and querying, based on e.g. the r-index and move structure. His group's methods have been cited over 130,000 times, and he is the winner of awards including an NSF CAREER award, a Sloan Research Fellowship, the Benjamin Franklin award for contributions to open access, and multiple awards for teaching and mentorship. Ben is the founder and principal of InOrder Labs LLC (https://inorderlabs.com), an expert consulting firm in bioinformatics and computational biology. Channel: / @benlangmead Teaching materials: https://langmead-lab.org/teaching.html

High order empirical entropy

Bitvectors and rank/select

Huffman Codes: An Information Theory Perspective

Lecture 5: Entropy and Data Compression (IV): Shannon's Source Coding Theorem, Symbol Codes

these compression algorithms could halve our image file sizes (but we don't use them) #SoMEpi

Burrows-Wheeler Transform, part 1

Shannon Entropy and Information Gain

Overlap-Layout-Consensus (OLC) approach #swayamprabha #ch17sp

Information Theory Basics

Entropy in Compression - Computerphile

CountMin sketch, part 1

Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!

Entropy (for data science) Clearly Explained!!!

The World's Most Important Machine

1. Overview: information and entropy

40Hz Binaural Gamma Waves - Ultra Deep Concentration

Information entropy | Journey into information theory | Computer Science | Khan Academy

Lecture 3: Entropy and Data Compression (II): Shannon's Source Coding Theorem, The Bent Coin Lottery

FM Index, part 2: efficient matching

