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.
Этот курс входит в специализацию ''Специализация Структуры и алгоритмы данных'
Строковые алгоритмы
от партнера
Калифорнийский университет в Сан-Диего
Национальный исследовательский университет "Высшая школа экономики"
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Об этом курсе
22,752
22,752Приобретаемые навыки
Suffix TreeSuffix ArrayKnuth–Morris–Pratt (KMP) AlgorithmAlgorithms On Strings
Программа курса: что вы изучите
Неделя
14 ч. на завершение
Suffix 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 видео ((всего 27 мин.)), 5 материалов для самостоятельного изучения, 1 тест
5 видео
Brute Force Approach to Pattern Matching2мин
Herding Patterns into Trie5мин
Herding Text into Suffix Trie6мин
Suffix Trees4мин
5 материала для самостоятельного изучения
Trie Construction - Pseudocode10мин
FAQ10мин
Slides and External References10мин
Available Programming Languages10мин
FAQ on Programming Assignments10мин
Неделя
24 ч. на завершение
Burrows-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 видео ((всего 30 мин.)), 4 материалов для самостоятельного изучения, 1 тест
5 видео
Inverting Burrows-Wheeler Transform7мин
Using BWT for Pattern Matching6мин
Suffix Arrays5мин
Approximate Pattern Matching6мин
4 материала для самостоятельного изучения
Using BWT for Pattern Matching10мин
Pattern Matching with Suffix Array10мин
FAQ10мин
Slides and External References10мин
Неделя
33 ч. на завершение
Knuth–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 видео ((всего 35 мин.)), 2 материалов для самостоятельного изучения, 1 тест
5 видео
Safe Shift3мин
Prefix Function7мин
Computing Prefix Function9мин
Knuth-Morris-Pratt Algorithm5мин
2 материала для самостоятельного изучения
Programming Assignment 3 lasts for two weeks
Slides and External References10мин
1 практическое упражнение
Exact Pattern Matching30мин
Неделя
45 ч. на завершение
Constructing 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 видео ((всего 76 мин.)), 5 материалов для самостоятельного изучения, 2 тестов
11 видео
Suffix Array6мин
General Strategy6мин
Initialization9мин
Sort Doubled Cyclic Shifts8мин
SortDouble Implementation6мин
Updating Classes8мин
Full Algorithm3мин
Suffix Array and Suffix Tree8мин
LCP Array5мин
Computing the LCP Array6мин
Construct Suffix Tree from Suffix Array and LCP Array6мин
5 материала для самостоятельного изучения
Counting Sort10мин
Slides and External References2мин
Computing the LCP Array - Additional Slides10мин
Suffix Tree Construction - Pseudocode10мин
Slides and External References2мин
1 практическое упражнение
Suffix Array Construction12мин
50%
начал новую карьеру, пройдя эти курсы
42%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
Excelentes algoritmos, nunca había pensado lo complicado que era hacer pattern matching eficientemente en términos de tiempo de procesamiento y memoria.
great course, interesting concepts and very well delivered content from lecture videos. challenging and rewarding programming assignments.
Преподаватели
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
О Калифорнийский университет в Сан-Диего
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.
О Национальный исследовательский университет "Высшая школа экономики"
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 communicamathematics, engineering, and more.
Learn more on www.hse.ru
О специализации ''Структуры и алгоритмы данных'
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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