Acerca de este Curso
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Nivel intermedio

Aprox. 35 horas para completar

Sugerido: 6 weeks of study, 6–10 hours per week....

Inglés (English)

Subtítulos: Inglés (English), Coreano

Habilidades que obtendrás

GraphsData StructureAlgorithmsData Compression
Los estudiantes que toman este Course son
  • Software Engineers
  • Machine Learning Engineers
  • Data Scientists
  • Data Engineers
  • Systems Engineers

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 35 horas para completar

Sugerido: 6 weeks of study, 6–10 hours per week....

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
10 minutos para completar

Introduction

1 video (Total 9 minutos), 2 lecturas
1 video
2 lecturas
Welcome to Algorithms, Part II1m
Lecture Slides
2 horas para completar

Undirected Graphs

6 videos (Total 98 minutos), 2 lecturas, 1 cuestionario
6 videos
Graph API14m
Depth-First Search26m
Breadth-First Search13m
Connected Components18m
Graph Challenges14m
2 lecturas
Overview1m
Lecture Slides
1 ejercicio de práctica
Interview Questions: Undirected Graphs (ungraded)6m
9 horas para completar

Directed Graphs

5 videos (Total 68 minutos), 1 lectura, 2 cuestionarios
5 videos
Digraph API4m
Digraph Search20m
Topological Sort 12m
Strong Components20m
1 lectura
Lecture Slides
1 ejercicio de práctica
Interview Questions: Directed Graphs (ungraded)6m
Semana
2
2 horas para completar

Minimum Spanning Trees

6 videos (Total 85 minutos), 2 lecturas, 1 cuestionario
6 videos
Greedy Algorithm12m
Edge-Weighted Graph API11m
Kruskal's Algorithm12m
Prim's Algorithm33m
MST Context10m
2 lecturas
Overview1m
Lecture Slides
1 ejercicio de práctica
Interview Questions: Minimum Spanning Trees (ungraded)6m
10 horas para completar

Shortest Paths

5 videos (Total 85 minutos), 1 lectura, 2 cuestionarios
5 videos
Shortest Path Properties14m
Dijkstra's Algorithm18m
Edge-Weighted DAGs19m
Negative Weights21m
1 lectura
Lecture Slides
1 ejercicio de práctica
Interview Questions: Shortest Paths (ungraded)6m
Semana
3
7 horas para completar

Maximum Flow and Minimum Cut

6 videos (Total 72 minutos), 2 lecturas, 2 cuestionarios
6 videos
Ford–Fulkerson Algorithm6m
Maxflow–Mincut Theorem9m
Running Time Analysis8m
Java Implementation14m
Maxflow Applications22m
2 lecturas
Overview
Lecture Slides
1 ejercicio de práctica
Interview Questions: Maximum Flow (ungraded)6m
2 horas para completar

Radix Sorts

6 videos (Total 85 minutos), 1 lectura, 1 cuestionario
6 videos
Key-Indexed Counting12m
LSD Radix Sort15m
MSD Radix Sort13m
3-way Radix Quicksort7m
Suffix Arrays19m
1 lectura
Lecture Slides
1 ejercicio de práctica
Interview Questions: Radix Sorts (ungraded)6m
Semana
4
2 horas para completar

Tries

3 videos (Total 75 minutos), 2 lecturas, 1 cuestionario
3 videos
Ternary Search Tries22m
Character-Based Operations20m
2 lecturas
Overview10m
Lecture Slides
1 ejercicio de práctica
Interview Questions: Tries (ungraded)6m
10 horas para completar

Substring Search

5 videos (Total 75 minutos), 1 lectura, 2 cuestionarios
5 videos
Brute-Force Substring Search10m
Knuth–Morris–Pratt33m
Boyer–Moore8m
Rabin–Karp16m
1 lectura
Lecture Slides10m
1 ejercicio de práctica
Interview Questions: Substring Search (ungraded)6m
5.0
140 revisionesChevron Right

15%

comenzó una nueva carrera después de completar estos cursos

20%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

13%

consiguió un aumento de sueldo o ascenso

Principales revisiones sobre Algorithms, Part II

por IOJan 21st 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 AKApr 17th 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.

Instructores

Avatar

Robert Sedgewick

William O. Baker *39 Professor of Computer Science
Computer Science
Avatar

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer
Computer Science

Acerca de Universidad 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....

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • No. All features of this course are available for free.

  • No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with 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.

  • 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.

  • 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.

  • 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.

  • 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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