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.
제공자:
캘리포니아 샌디에고 대학교
국립 연구 고등 경제 대학
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About this Course
22,145
22,145귀하가 습득할 기술
Suffix TreeSuffix ArrayKnuth–Morris–Pratt (KMP) AlgorithmAlgorithms On Strings
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 4시간 필요
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 videos (Total 27 min), 5 readings, 1 quiz
5개의 동영상
Brute Force Approach to Pattern Matching2m
Herding Patterns into Trie5m
Herding Text into Suffix Trie6m
Suffix Trees4m
5개의 읽기 자료
Trie Construction - Pseudocode10m
FAQ10m
Slides and External References10m
Available Programming Languages10m
FAQ on Programming Assignments10m
주
2완료하는 데 4시간 필요
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 videos (Total 30 min), 4 readings, 1 quiz
5개의 동영상
Inverting Burrows-Wheeler Transform7m
Using BWT for Pattern Matching6m
Suffix Arrays5m
Approximate Pattern Matching6m
4개의 읽기 자료
Using BWT for Pattern Matching10m
Pattern Matching with Suffix Array10m
FAQ10m
Slides and External References10m
주
3완료하는 데 3시간 필요
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 videos (Total 35 min), 2 readings, 1 quiz
5개의 동영상
Safe Shift3m
Prefix Function7m
Computing Prefix Function9m
Knuth-Morris-Pratt Algorithm5m
2개의 읽기 자료
Programming Assignment 3 lasts for two weeks
Slides and External References10m
1개 연습문제
Exact Pattern Matching30m
주
4완료하는 데 5시간 필요
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 videos (Total 76 min), 5 readings, 2 quizzes
11개의 동영상
General Strategy6m
Initialization9m
Sort Doubled Cyclic Shifts8m
SortDouble Implementation6m
Updating Classes8m
Full Algorithm3m
Suffix Array and Suffix Tree8m
LCP Array5m
Computing the LCP Array6m
Construct Suffix Tree from Suffix Array and LCP Array6m
5개의 읽기 자료
Counting Sort10m
Slides and External References2m
Computing the LCP Array - Additional Slides10m
Suffix Tree Construction - Pseudocode10m
Slides and External References2m
1개 연습문제
Suffix Array Construction12m
4.5
103개의 리뷰50%
이 강좌를 수료한 후 새로운 경력 시작하기
42%
이 강좌를 통해 확실한 경력상 이점 얻기
최상위 리뷰
great course, interesting concepts and very well delivered content from lecture videos. challenging and rewarding programming assignments.
Learned a lot in the class. It was really excellent. But many a times we need to hear twice to understand some of the concepts clearly.
강사
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
캘리포니아 샌디에고 대학교 정보
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.
국립 연구 고등 경제 대학 정보
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
데이터 구조 및 알고리즘 전문 분야 정보
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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