This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
oferecido por
Alogarítimos, Parte II
Universidade de PrincetonInformações sobre o curso
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Resultados de carreira do aprendiz
18%
comecei uma nova carreira após concluir estes cursos
22%
consegui um benefício significativo de carreira com este curso
16%
recebi um aumento ou promoção
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 58 horas para completar
Inglês
Legendas: Inglês, Coreano
Habilidades que você terá
GraphsData StructureAlgorithmsData Compression
Resultados de carreira do aprendiz
18%
comecei uma nova carreira após concluir estes cursos
22%
consegui um benefício significativo de carreira com este curso
16%
recebi um aumento ou promoção
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 58 horas para completar
Inglês
Legendas: Inglês, Coreano
oferecido por
Universidade de Princeton
Princeton University is a private research university located in Princeton, New Jersey, United States. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution.
Programa - O que você aprenderá com este curso
10 minutos para concluir
Introduction
Welcome to Algorithms, Part II.
10 minutos para concluir
1 vídeo (Total 9 mín.), 2 leituras
1 vídeos
2 leituras
Welcome to Algorithms, Part II1min
Lecture Slides
2 horas para concluir
Undirected Graphs
We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications.
2 horas para concluir
6 vídeos (Total 98 mín.), 2 leituras, 1 teste
6 videos
Graph API14min
Depth-First Search26min
Breadth-First Search13min
Connected Components18min
Graph Challenges14min
2 leituras
Overview1min
Lecture Slides
1 exercício prático
Interview Questions: Undirected Graphs (ungraded)6min
9 horas para concluir
Directed Graphs
In this lecture we study directed graphs. We begin with depth-first search and breadth-first search in digraphs and describe applications ranging from garbage collection to web crawling. Next, we introduce a depth-first search based algorithm for computing the topological order of an acyclic digraph. Finally, we implement the Kosaraju−Sharir algorithm for computing the strong components of a digraph.
9 horas para concluir
5 vídeos (Total 68 mín.), 1 leitura, 2 testes
5 videos
Digraph API4min
Digraph Search20min
Topological Sort 12min
Strong Components20min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Directed Graphs (ungraded)6min
2 horas para concluir
Minimum Spanning Trees
In this lecture we study the minimum spanning tree problem. We begin by considering a generic greedy algorithm for the problem. Next, we consider and implement two classic algorithm for the problem—Kruskal's algorithm and Prim's algorithm. We conclude with some applications and open problems.
2 horas para concluir
6 vídeos (Total 85 mín.), 2 leituras, 1 teste
6 videos
Greedy Algorithm12min
Edge-Weighted Graph API11min
Kruskal's Algorithm12min
Prim's Algorithm33min
MST Context10min
2 leituras
Overview1min
Lecture Slides
1 exercício prático
Interview Questions: Minimum Spanning Trees (ungraded)6min
10 horas para concluir
Shortest Paths
In this lecture we study shortest-paths problems. We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative. We conclude with the Bellman−Ford−Moore algorithm for edge-weighted digraphs with no negative cycles. We also consider applications ranging from content-aware fill to arbitrage.
10 horas para concluir
5 vídeos (Total 85 mín.), 1 leitura, 2 testes
5 videos
Shortest Paths APIs10min
Shortest Path Properties14min
Dijkstra's Algorithm18min
Edge-Weighted DAGs19min
Negative Weights21min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Shortest Paths (ungraded)6min
7 horas para concluir
Maximum Flow and Minimum Cut
In this lecture we introduce the maximum flow and minimum cut problems. We begin with the Ford−Fulkerson algorithm. To analyze its correctness, we establish the maxflow−mincut theorem. Next, we consider an efficient implementation of the Ford−Fulkerson algorithm, using the shortest augmenting path rule. Finally, we consider applications, including bipartite matching and baseball elimination.
7 horas para concluir
6 vídeos (Total 72 mín.), 2 leituras, 2 testes
6 videos
Ford–Fulkerson Algorithm6min
Maxflow–Mincut Theorem9min
Running Time Analysis8min
Java Implementation14min
Maxflow Applications22min
2 leituras
Overview
Lecture Slides
1 exercício prático
Interview Questions: Maximum Flow (ungraded)6min
2 horas para concluir
Radix Sorts
In this lecture we consider specialized sorting algorithms for strings and related objects. We begin with a subroutine to sort integers in a small range. We then consider two classic radix sorting algorithms—LSD and MSD radix sorts. Next, we consider an especially efficient variant, which is a hybrid of MSD radix sort and quicksort known as 3-way radix quicksort. We conclude with suffix sorting and related applications.
2 horas para concluir
6 vídeos (Total 85 mín.), 1 leitura, 1 teste
6 videos
Strings in Java17min
Key-Indexed Counting12min
LSD Radix Sort15min
MSD Radix Sort13min
3-way Radix Quicksort7min
Suffix Arrays19min
1 leituras
Lecture Slides
1 exercício prático
Interview Questions: Radix Sorts (ungraded)6min
2 horas para concluir
Tries
In this lecture we consider specialized algorithms for symbol tables with string keys. Our goal is a data structure that is as fast as hashing and even more flexible than binary search trees. We begin with multiway tries; next we consider ternary search tries. Finally, we consider character-based operations, including prefix match and longest prefix, and related applications.
2 horas para concluir
3 vídeos (Total 75 mín.), 2 leituras, 1 teste
2 leituras
Overview10min
Lecture Slides
1 exercício prático
Interview Questions: Tries (ungraded)6min
10 horas para concluir
Substring Search
In this lecture we consider algorithms for searching for a substring in a piece of text. We begin with a brute-force algorithm, whose running time is quadratic in the worst case. Next, we consider the ingenious Knuth−Morris−Pratt algorithm whose running time is guaranteed to be linear in the worst case. Then, we introduce the Boyer−Moore algorithm, whose running time is sublinear on typical inputs. Finally, we consider the Rabin−Karp fingerprint algorithm, which uses hashing in a clever way to solve the substring search and related problems.
10 horas para concluir
5 vídeos (Total 75 mín.), 1 leitura, 2 testes
5 videos
Brute-Force Substring Search10min
Knuth–Morris–Pratt33min
Boyer–Moore8min
Rabin–Karp16min
1 leituras
Lecture Slides10min
1 exercício prático
Interview Questions: Substring Search (ungraded)6min
Avaliações
Principais avaliações do ALOGARÍTIMOS, PARTE II
por IO20 de Jan de 2018
Pretty challenging course, but very good. Having a book is a must (at least it was for me), video lectures complement book nicely, and some topics are explained better in the Algorithms, 4th ed. book.
por HT7 de Fev de 2020
The algorithms are more difficult than part I, nevertheless Sedgewick's vids are still easy to understand. The only drawback maybe chapter 3, max flow min cut part, which is not very clarified.
por AK16 de Abr de 2019
Amazing course! Loved the theory and exercises! Just a note for others: Its part 1 had almost no dependency on book, but this part 2 has some dependency (e.g. chapter on Graph) on book as well.
por RK25 de Set de 2018
An incredible course that covers a lot of vital algorithm on graphs and strings. I learned a lot of new material that I hadn't known before. Thank you very much for this amazing course!
Perguntas Frequentes – FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When will I have access to the lectures and assignments?
Once you enroll, you’ll have access to all videos and programming assignments.
Do I need to pay for this course?
No. All features of this course are available for free.
Can I earn a certificate in this course?
No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course.
I have no familiarity with Java programming. Can I still take this course?
Our central thesis is that algorithms are best understood by implementing and testing them. Our use of Java is essentially expository, and we shy away from exotic language features, so we expect you would be able to adapt our code to your favorite language. However, we require that you submit the programming assignments in Java.
Which algorithms and data structures are covered in this course?
Part II focuses on graph and string-processing algorithms. Topics include depth-first search, breadth-first search, topological sort, Kosaraju−Sharir, Kruskal, Prim, Dijkistra, Bellman−Ford, Ford−Fulkerson, LSD radix sort, MSD radix sort, 3-way radix quicksort, multiway tries, ternary search tries, Knuth−Morris−Pratt, Boyer−Moore, Rabin−Karp, regular expression matching, run-length coding, Huffman coding, LZW compression, and the Burrows−Wheeler transform.
Part I focuses on elementary data structures, sorting, and searching. Topics include union-find, binary search, stacks, queues, bags, insertion sort, selection sort, shellsort, quicksort, 3-way quicksort, mergesort, heapsort, binary heaps, binary search trees, red−black trees, separate-chaining and linear-probing hash tables, Graham scan, and kd-trees.
What kinds of assessments are available in this course?
Weekly programming assignments and interview questions.
The programming assignments involve either implementing algorithms and data structures (graph algorithms, tries, and the Burrows–Wheeler transform) or applying algorithms and data structures to an interesting domain (computer graphics, computational linguistics, and data compression). The assignments are evaluated using a sophisticated autograder that provides detailed feedback about style, correctness, and efficiency.
The interview questions are similar to those that you might find at a technical job interview. They are optional and not graded.
I am/was not a Computer Science major. Is this course for me?
This course is for anyone using a computer to address large problems (and therefore needing efficient algorithms). At Princeton, over 25% of all students take the course, including people majoring in engineering, biology, physics, chemistry, economics, and many other fields, not just computer science.
How does this course differ from Design and Analysis of Algorithms?
The two courses are complementary. This one is essentially a programming course that concentrates on developing code; that one is essentially a math course that concentrates on understanding proofs. This course is about learning algorithms in the context of implementing and testing them in practical applications; that one is about learning algorithms in the context of developing mathematical models that help explain why they are efficient. In typical computer science curriculums, a course like this one is taken by first- and second-year students and a course like that one is taken by juniors and seniors.
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