Algoritmos, Parte I

Universidade de Princeton

Informações sobre o curso

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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.
All the features of this course are available for free.  It does not offer a certificate upon completion.

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consegui um benefício significativo de carreira com este curso

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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. 53 horas para completar

Inglês

Legendas: Francês, Português (Brasil), Coreano, Russo, Inglês, Espanhol

Habilidades que você terá

Data StructureAlgorithmsJava Programming

Resultados de carreira do aprendiz

30%

comecei uma nova carreira após concluir estes cursos

31%

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. 53 horas para completar

Inglês

Legendas: Francês, Português (Brasil), Coreano, Russo, Inglês, Espanhol

Instrutores

Classificação do instrutor

4.79/5 (749 classificações)

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer

Computer Science

867,220 aprendizes

4 Cursos

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

889,133 aprendizes

6 Cursos

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

Classificação do conteúdo98%(54,570 classificações)

Semana

Semana 1

10 minutos para concluir

Course Introduction

Welcome to Algorithms, Part I.

10 minutos para concluir

1 vídeo (Total 9 mín.), 2 leituras

1 vídeos

Course Introduction9min

2 leituras

Welcome to Algorithms, Part I1min

Lecture Slides

9 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.

9 horas para concluir

5 vídeos (Total 51 mín.), 2 leituras, 2 testes

5 videos

Dynamic Connectivity10min

Quick Find10min

Quick Union7min

Quick-Union Improvements13min

Union−Find Applications9min

2 leituras

Overview1min

Lecture Slides

1 exercício prático

Interview Questions: Union–Find (ungraded)

1 hora 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.

1 hora para concluir

6 vídeos (Total 66 mín.), 1 leitura, 1 teste

6 videos

Analysis of Algorithms Introduction8min

Observations10min

Mathematical Models12min

Order-of-Growth Classifications14min

Theory of Algorithms11min

Memory8min

1 leituras

Lecture Slides

1 exercício prático

Interview Questions: Analysis of Algorithms (ungraded)

Semana

Semana 2

9 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.

9 horas para concluir

6 vídeos (Total 61 mín.), 2 leituras, 2 testes

6 videos

Stacks16min

Resizing Arrays9min

Queues4min

Generics9min

Iterators7min

Stack and Queue Applications (optional)13min

2 leituras

Overview1min

Lecture Slides

1 exercício prático

Interview Questions: Stacks and Queues (ungraded)

1 hora 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.

1 hora para concluir

6 vídeos (Total 63 mín.), 1 leitura, 1 teste

6 videos

Sorting Introduction14min

Selection Sort6min

Insertion Sort9min

Shellsort10min

Shuffling7min

Convex Hull13min

1 leituras

Lecture Slides

1 exercício prático

Interview Questions: Elementary Sorts (ungraded)

Semana

Semana 3

9 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.

9 horas para concluir

5 vídeos (Total 49 mín.), 2 leituras, 2 testes

5 videos

Mergesort23min

Bottom-up Mergesort3min

Sorting Complexity9min

Comparators6min

Stability5min

2 leituras

Overview

Lecture Slides

1 exercício prático

Interview Questions: Mergesort (ungraded)

1 hora 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.

1 hora para concluir

4 vídeos (Total 50 mín.), 1 leitura, 1 teste

4 videos

Quicksort19min

Selection7min

Duplicate Keys11min

System Sorts11min

1 leituras

Lecture Slides

1 exercício prático

Interview Questions: Quicksort (ungraded)

Semana

Semana 4

9 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.

9 horas para concluir

4 vídeos (Total 74 mín.), 2 leituras, 2 testes

4 videos

APIs and Elementary Implementations12min

Binary Heaps23min

Heapsort14min

Event-Driven Simulation (optional)22min

2 leituras

Overview10min

Lecture Slides

1 exercício prático

Interview Questions: Priority Queues (ungraded)

2 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.

2 horas para concluir

6 vídeos (Total 77 mín.), 1 leitura, 1 teste

6 videos

Symbol Table API21min

Elementary Implementations9min

Ordered Operations6min

Binary Search Trees19min

Ordered Operations in BSTs10min

Deletion in BSTs9min

1 leituras

Lecture Slides

1 exercício prático

Interview Questions: Elementary Symbol Tables (ungraded)30min

Semana

Semana 5

2 horas para concluir

Balanced Search Trees

In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2−3 trees, which are easy to analyze but hard to implement. Next, we consider red−black binary search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems.

2 horas para concluir

3 vídeos (Total 63 mín.), 2 leituras, 1 teste

3 videos

2−3 Search Trees16min

Red-Black BSTs35min

B-Trees (optional)10min

2 leituras

Overview10min

Lecture Slides

1 exercício prático

Interview Questions: Balanced Search Trees (ungraded)30min

9 horas para concluir

Geometric Applications of BSTs

We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kd-trees, a natural generalization of BSTs when the keys are points in the plane (or higher dimensions). We also consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles.

9 horas para concluir

5 vídeos (Total 66 mín.), 1 leitura, 1 teste

5 videos

1d Range Search8min

Line Segment Intersection5min

Kd-Trees29min

Interval Search Trees13min

Rectangle Intersection8min

1 leituras

Lecture Slides

Semana

Semana 6

1 hora para concluir

Hash Tables

We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption.

1 hora para concluir

4 vídeos (Total 50 mín.), 2 leituras, 1 teste

4 videos

Hash Tables18min

Separate Chaining7min

Linear Probing14min

Hash Table Context10min

2 leituras

Overview10min

Lecture Slides

1 exercício prático

Interview Questions: Hash Tables (ungraded)

26 minutos para concluir

Symbol Table Applications

We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.

26 minutos para concluir

4 vídeos (Total 26 mín.), 1 leitura

4 videos

Symbol Table Applications: Sets (optional)5min

Symbol Table Applications: Dictionary Clients (optional)5min

Symbol Table Applications: Indexing Clients (optional)7min

Symbol Table Applications: Sparse Vectors (optional)7min

1 leituras

Lecture Slides

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
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 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.
Which kinds of assessments are available in this course?
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.
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.
Ganharei créditos universitários por concluir o curso?
Este curso não oferece créditos universitários, mas algumas universidades podem aceitar certificados de cursos que podem ser convertidos em créditos. Entre em contato com sua instituição para saber mais. Com os cursos on-line e os certificados Mastertrack™ do Coursera, é possível ganhar créditos universitários.

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Algoritmos, Parte I