World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome.
Ce cours fait partie de la Spécialisation Data Structures and Algorithms
Algorithms on Strings
proposé par
University of California San Diego
National Research University Higher School of Economics
À propos de ce cours
4.5
480 notes
Compétences que vous acquerrez
Suffix TreeSuffix ArrayKnuth–Morris–Pratt (KMP) AlgorithmAlgorithms On Strings
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Suffix Trees
How would you search for a longest repeat in a string in LINEAR time? In 1973, Peter Weiner came up with a surprising solution that was based on suffix trees, the key data structure in pattern matching. Computer scientists were so impressed with his algorithm that they called it the Algorithm of the Year. In this lesson, we will explore some key ideas for pattern matching that will - through a series of trials and errors - bring us to suffix trees.
5 vidéos (Total 27 min), 5 lectures, 1 quiz
5 vidéos
Brute Force Approach to Pattern Matching2 min
Herding Patterns into Trie5 min
Herding Text into Suffix Trie6 min
Suffix Trees4 min
5 lectures
Trie Construction - Pseudocode10 min
FAQ10 min
Slides and External References10 min
Available Programming Languages10 min
FAQ on Programming Assignments10 min
Semaine
2Burrows-Wheeler Transform and Suffix Arrays
Although EXACT pattern matching with suffix trees is fast, it is not clear how to use suffix trees for APPROXIMATE pattern matching. In 1994, Michael Burrows and David Wheeler invented an ingenious algorithm for text compression that is now known as Burrows-Wheeler Transform. They knew nothing about genomics, and they could not have imagined that 15 years later their algorithm will become the workhorse of biologists searching for genomic mutations. But what text compression has to do with pattern matching??? In this lesson you will learn that the fate of an algorithm is often hard to predict – its applications may appear in a field that has nothing to do with the original plan of its inventors.
5 vidéos (Total 30 min), 4 lectures, 1 quiz
5 vidéos
Inverting Burrows-Wheeler Transform7 min
Using BWT for Pattern Matching6 min
Suffix Arrays5 min
Approximate Pattern Matching6 min
4 lectures
Using BWT for Pattern Matching10 min
Pattern Matching with Suffix Array10 min
FAQ10 min
Slides and External References10 min
Semaine
3Knuth–Morris–Pratt Algorithm
Congratulations, you have now learned the key pattern matching concepts: tries, suffix trees, suffix arrays and even the Burrows-Wheeler transform! However, some of the results Pavel mentioned remain mysterious: e.g., how can we perform exact pattern matching in O(|Text|) time rather than in O(|Text|*|Pattern|) time as in the naïve brute force algorithm? How can it be that matching a 1000-nucleotide pattern against the human genome is nearly as fast as matching a 3-nucleotide pattern??? Also, even though Pavel showed how to quickly construct the suffix array given the suffix tree, he has not revealed the magic behind the fast algorithms for the suffix tree construction!In this module, Miсhael will address some algorithmic challenges that Pavel tried to hide from you :) such as the Knuth-Morris-Pratt algorithm for exact pattern matching and more efficient algorithms for suffix tree and suffix array construction.
5 vidéos (Total 35 min), 2 lectures, 1 quiz
5 vidéos
Safe Shift3 min
Prefix Function7 min
Computing Prefix Function9 min
Knuth-Morris-Pratt Algorithm5 min
2 lectures
Programming Assignment 3 lasts for two weeks min
Slides and External References10 min
1 exercice pour s'entraîner
Exact Pattern Matching30 min
Semaine
4Constructing Suffix Arrays and Suffix Trees
In this module we continue studying algorithmic challenges of the string algorithms. You will learn an O(n log n) algorithm for suffix array construction and a linear time algorithm for construction of suffix tree from a suffix array. You will also implement these algorithms and the Knuth-Morris-Pratt algorithm in the last Programming Assignment in this course.
11 vidéos (Total 76 min), 5 lectures, 2 quiz
11 vidéos
Suffix Array6 min
General Strategy6 min
Initialization9 min
Sort Doubled Cyclic Shifts8 min
SortDouble Implementation6 min
Updating Classes8 min
Full Algorithm3 min
Suffix Array and Suffix Tree8 min
LCP Array5 min
Computing the LCP Array6 min
Construct Suffix Tree from Suffix Array and LCP Array6 min
5 lectures
Counting Sort10 min
Slides and External References2 min
Computing the LCP Array - Additional Slides10 min
Suffix Tree Construction - Pseudocode10 min
Slides and External References2 min
1 exercice pour s'entraîner
Suffix Array Construction12 min
4.5
97 avis75%
a commencé une nouvelle carrière après avoir terminé ces cours
83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
par HC•May 7th 2018
Excelentes algoritmos, nunca había pensado lo complicado que era hacer pattern matching eficientemente en términos de tiempo de procesamiento y memoria.
par PG•Nov 19th 2017
great course, interesting concepts and very well delivered content from lecture videos. challenging and rewarding programming assignments.
Enseignants
Alexander S. Kulikov
Visiting ProfessorDepartment of Computer Science and Engineering
Michael Levin
LecturerComputer Science
Pavel Pevzner
ProfessorDepartment of Computer Science and Engineering
Neil Rhodes
Adjunct FacultyComputer Science and Engineering
À propos de University of California San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
À propos de National Research University Higher School of Economics
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru
À propos de la Spécialisation Data Structures and Algorithms
This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.
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