Informações sobre o curso
4.9
4,319 classificações
934 avaliações
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 31 horas para completar

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

Inglês

Legendas: Inglês, Coreano

Habilidades que você terá

Data StructurePriority QueueAlgorithmsJava Programming
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 31 horas para completar

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

Inglês

Legendas: Inglês, Coreano

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
10 minutos para concluir

Course Introduction

Welcome to Algorithms, Part I....
Reading
1 vídeos (total de (Total 9 mín.) min), 2 leituras
Video1 videos
Reading2 leituras
Welcome to Algorithms, Part I1min
Lecture Slides
Horas para completar
6 horas para concluir

Union−Find

We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union−find data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the union−find data type to the percolation problem from physical chemistry....
Reading
5 vídeos (total de (Total 51 mín.) min), 2 leituras, 2 testes
Video5 videos
Quick Find10min
Quick Union7min
Quick-Union Improvements13min
Union−Find Applications9min
Reading2 leituras
Overview1min
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Union–Find (ungraded)
Horas para completar
1 horas para concluir

Analysis of Algorithms

The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs....
Reading
6 vídeos (total de (Total 66 mín.) min), 1 leitura, 1 teste
Video6 videos
Observations10min
Mathematical Models12min
Order-of-Growth Classifications14min
Theory of Algorithms11min
Memory8min
Reading1 leituras
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Analysis of Algorithms (ungraded)
Semana
2
Horas para completar
6 horas para concluir

Stacks and Queues

We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems....
Reading
6 vídeos (total de (Total 61 mín.) min), 2 leituras, 2 testes
Video6 videos
Stacks16min
Resizing Arrays9min
Queues4min
Generics9min
Iterators7min
Stack and Queue Applications (optional)13min
Reading2 leituras
Overview1min
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Stacks and Queues (ungraded)
Horas para completar
1 horas para concluir

Elementary Sorts

We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm....
Reading
6 vídeos (total de (Total 63 mín.) min), 1 leitura, 1 teste
Video6 videos
Selection Sort6min
Insertion Sort9min
Shellsort10min
Shuffling7min
Convex Hull13min
Reading1 leituras
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Elementary Sorts (ungraded)
Semana
3
Horas para completar
6 horas para concluir

Mergesort

We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottom-up version. We prove that any compare-based sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability....
Reading
5 vídeos (total de (Total 49 mín.) min), 2 leituras, 2 testes
Video5 videos
Bottom-up Mergesort3min
Sorting Complexity9min
Comparators6min
Stability5min
Reading2 leituras
Overview
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Mergesort (ungraded)
Horas para completar
1 horas para concluir

Quicksort

We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3-way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys....
Reading
4 vídeos (total de (Total 50 mín.) min), 1 leitura, 1 teste
Video4 videos
Selection7min
Duplicate Keys11min
System Sorts11min
Reading1 leituras
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Quicksort (ungraded)
Semana
4
Horas para completar
6 horas para concluir

Priority Queues

We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision. ...
Reading
4 vídeos (total de (Total 74 mín.) min), 2 leituras, 2 testes
Video4 videos
Binary Heaps23min
Heapsort14min
Event-Driven Simulation (optional)22min
Reading2 leituras
Overview10min
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Priority Queues (ungraded)
Horas para completar
1 horas para concluir

Elementary Symbol Tables

We define an API for symbol tables (also known as associative arrays, maps, or dictionaries) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance....
Reading
6 vídeos (total de (Total 77 mín.) min), 1 leitura, 1 teste
Video6 videos
Elementary Implementations9min
Ordered Operations6min
Binary Search Trees19min
Ordered Operations in BSTs10min
Deletion in BSTs9min
Reading1 leituras
Lecture Slides
Quiz1 exercícios práticos
Interview Questions: Elementary Symbol Tables (ungraded)8min

Instrutores

Avatar

Kevin Wayne

Senior Lecturer
Computer Science
Avatar

Robert Sedgewick

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

Sobre Princeton University

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....

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você adquire o Certificado, ganha acesso a todo o material do curso, incluindo avaliações com nota atribuída. Após concluir o curso, seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

  • 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 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.

    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.

  • Weekly exercises, weekly programming assignments, weekly interview questions, and a final exam.

    The exercises are primarily composed of short drill questions (such as tracing the execution of an algorithm or data structure), designed to help you master the material.

    The programming assignments involve either implementing algorithms and data structures (deques, randomized queues, and kd-trees) or applying algorithms and data structures to an interesting domain (computational chemistry, computational geometry, and mathematical recreation). 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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