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. In this online course you will learn key pattern matching concepts: tries, suffix trees, suffix arrays and even the Burrows-Wheeler transform.
This course is part of the Data Structures and Algorithms Specialization
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Data Structures and Algorithms SpecializationUniversity of California San DiegoAbout this Course
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Course 4 of 6 in the
Intermediate Level
Approx. 19 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Skills you will gain
- Suffix Tree
- Suffix Array
- Knuth–Morris–Pratt (KMP) Algorithm
- Algorithms On Strings
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Intermediate Level
Approx. 19 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
5 hours to complete
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 hours to complete
6 videos (Total 32 min), 5 readings, 2 quizzes
5 hours to complete
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 hours to complete
5 videos (Total 30 min), 4 readings, 2 quizzes
4 hours to complete
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.
4 hours to complete
8 videos (Total 54 min), 2 readings, 1 quiz
6 hours to complete
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.
6 hours to complete
16 videos (Total 119 min), 3 readings, 2 quizzes
Reviews
- 5 stars66.16%
- 4 stars21.95%
- 3 stars7.88%
- 2 stars2.39%
- 1 star1.59%
TOP REVIEWS FROM ALGORITHMS ON STRINGS
by SSJan 24, 2017
Initially the accent was a little bit hard to understand, but after few minutes everything become crystal clear. Extremely useful course content.
by JMNov 13, 2016
It would be nice if you could deliver the problem's solutions after the course ended. I ran out of ideas for the BetterBWMatching problem and i couldn't overcome the time issues.
by ABOct 20, 2017
Its a very good course overall. Just felt that towards the end the material got too much to digest, might be a good idea to split the contents of weeks 3 and 4 into 3 or 4 weeks.
by TNOct 4, 2019
The professor explained clearly as usual. The first 3 weeks is not too hard but final week took me really long time to understand.
About the Data Structures and Algorithms Specialization
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
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