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.

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 35 horas para completar

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

Inglês

Legendas: Inglês, Coreano

Habilidades que você terá

GraphsData StructureAlgorithmsData Compression

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 35 horas para completar

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

Inglês

Legendas: Inglês, Coreano

Programa - O que você aprenderá com este curso

Semana

1

10 minutos para concluir

Introduction

Welcome to Algorithms, Part II.

1 vídeo (Total 9 mín.), 2 leituras

1 vídeos

Course Introduction9min

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.

6 vídeos (Total 98 mín.), 2 leituras, 1 teste

6 videos

Introduction to Graphs9min

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.

5 vídeos (Total 68 mín.), 1 leitura, 2 testes

5 videos

Introduction to Digraphs8min

Digraph API4min

Digraph Search20min

Topological Sort 12min

Strong Components20min

1 leituras

Lecture Slides

1 exercício prático

Interview Questions: Directed Graphs (ungraded)6min

Semana

2

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.

6 vídeos (Total 85 mín.), 2 leituras, 1 teste

6 videos

Introduction to MSTs4min

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.

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

Semana

3

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.

6 vídeos (Total 72 mín.), 2 leituras, 2 testes

6 videos

Introduction to Maxflow10min

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.

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

Semana

4

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.

3 vídeos (Total 75 mín.), 2 leituras, 1 teste

3 videos

R-way Tries32min

Ternary Search Tries22min

Character-Based Operations20min

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.

5 vídeos (Total 75 mín.), 1 leitura, 2 testes

5 videos

Introduction to Substring Search6min

Brute-Force Substring Search10min

Knuth–Morris–Pratt33min

Boyer–Moore8min

Rabin–Karp16min

1 leituras

Lecture Slides10min

1 exercício prático

Interview Questions: Substring Search (ungraded)6min

Semana

5

2 horas para concluir

Regular Expressions

A regular expression is a method for specifying a set of strings. Our topic for this lecture is the famous grep algorithm that determines whether a given text contains any substring from the set. We examine an efficient implementation that makes use of our digraph reachability implementation from Week 1.

5 vídeos (Total 83 mín.), 2 leituras, 1 teste

5 videos

Regular Expressions20min

REs and NFAs13min

NFA Simulation18min

NFA Construction11min

Regular Expression Applications20min

2 leituras

Overview10min

Lecture Slides10min

1 exercício prático

Interview Questions: Regular Expressions (ungraded)6min

10 horas para concluir

Data Compression

We study and implement several classic data compression schemes, including run-length coding, Huffman compression, and LZW compression. We develop efficient implementations from first principles using a Java library for manipulating binary data that we developed for this purpose, based on priority queue and symbol table implementations from earlier lectures.

4 vídeos (Total 80 mín.), 1 leitura, 2 testes

4 videos

Introduction to Data Compression22min

Run-Length Coding5min

Huffman Compression24min

LZW Compression27min

1 leituras

Lecture Slides10min

1 exercício prático

Interview Questions: Data Compression (ungraded)6min

Semana

6

1 hora para concluir

Reductions

Our lectures this week are centered on the idea of problem-solving models like maxflow and shortest path, where a new problem can be formulated as an instance of one of those problems, and then solved with a classic and efficient algorithm. To complete the course, we describe the classic unsolved problem from theoretical computer science that is centered on the concept of algorithm efficiency and guides us in the search for efficient solutions to difficult problems.

4 vídeos (Total 40 mín.), 2 leituras, 1 teste

4 videos

Introduction to Reductions9min

Designing Algorithms8min

Establishing Lower Bounds9min

Classifying Problems12min

2 leituras

Overview10min

Lecture Slides10min

1 exercício prático

Interview Questions: Reductions (ungraded)6min

1 hora para concluir

Linear Programming (optional)

The quintessential problem-solving model is known as linear programming, and the simplex method for solving it is one of the most widely used algorithms. In this lecture, we given an overview of this central topic in operations research and describe its relationship to algorithms that we have considered.

4 vídeos (Total 61 mín.), 1 leitura, 1 teste

4 videos

Brewer's Problem21min

Simplex Algorithm11min

Simplex Implementations16min

Linear Programming Reductions11min

1 leituras

Lecture Slides10min

1 exercício prático

Interview Questions: Linear Programming (ungraded)6min

2 horas para concluir

Intractability

Is there a universal problem-solving model to which all problems that we would like to solve reduce and for which we know an efficient algorithm? You may be surprised to learn that we do no know the answer to this question. In this lecture we introduce the complexity classes P, NP, and NP-complete, pose the famous P = NP question, and consider implications in the context of algorithms that we have treated in this course.

6 vídeos (Total 85 mín.), 1 leitura, 1 teste

6 videos

Introduction to Intractability17min

Search Problems10min

P vs. NP16min

Classifying Problems13min

NP-Completeness12min

Coping with Intractability 14min

1 leituras

Lecture Slides10min

1 exercício prático

Interview Questions: Intractability (ungraded)6min

Instrutores

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer

Computer Science

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
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.
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.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.

Alogarítimos, Parte II