About this Course
5.0
740개의 평가
118개의 리뷰

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 34시간 필요

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

영어

자막: 영어, 한국어

귀하가 습득할 기술

GraphsData StructureAlgorithmsData Compression

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 34시간 필요

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

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 10분 필요

Introduction

Welcome to Algorithms, Part II....
1 video (Total 9 min), 2 readings
1개의 동영상
2개의 읽기 자료
Welcome to Algorithms, Part II1m
Lecture Slides
완료하는 데 2시간 필요

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 videos (Total 98 min), 2 readings, 1 quiz
6개의 동영상
Graph API14m
Depth-First Search26m
Breadth-First Search13m
Connected Components18m
Graph Challenges14m
2개의 읽기 자료
Overview1m
Lecture Slides
1개 연습문제
Interview Questions: Undirected Graphs (ungraded)6m
완료하는 데 7시간 필요

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 videos (Total 68 min), 1 reading, 2 quizzes
5개의 동영상
Digraph API4m
Digraph Search20m
Topological Sort 12m
Strong Components20m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Directed Graphs (ungraded)6m
2
완료하는 데 2시간 필요

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 videos (Total 85 min), 2 readings, 1 quiz
6개의 동영상
Greedy Algorithm12m
Edge-Weighted Graph API11m
Kruskal's Algorithm12m
Prim's Algorithm33m
MST Context10m
2개의 읽기 자료
Overview1m
Lecture Slides
1개 연습문제
Interview Questions: Minimum Spanning Trees (ungraded)6m
완료하는 데 8시간 필요

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 videos (Total 85 min), 1 reading, 2 quizzes
5개의 동영상
Shortest Path Properties14m
Dijkstra's Algorithm18m
Edge-Weighted DAGs19m
Negative Weights21m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Shortest Paths (ungraded)6m
3
완료하는 데 7시간 필요

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 videos (Total 72 min), 2 readings, 2 quizzes
6개의 동영상
Ford–Fulkerson Algorithm6m
Maxflow–Mincut Theorem9m
Running Time Analysis8m
Java Implementation14m
Maxflow Applications22m
2개의 읽기 자료
Overview
Lecture Slides
1개 연습문제
Interview Questions: Maximum Flow (ungraded)6m
완료하는 데 2시간 필요

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 videos (Total 85 min), 1 reading, 1 quiz
6개의 동영상
Key-Indexed Counting12m
LSD Radix Sort15m
MSD Radix Sort13m
3-way Radix Quicksort7m
Suffix Arrays19m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Radix Sorts (ungraded)6m
4
완료하는 데 2시간 필요

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 videos (Total 75 min), 2 readings, 1 quiz
3개의 동영상
Ternary Search Tries22m
Character-Based Operations20m
2개의 읽기 자료
Overview10m
Lecture Slides
1개 연습문제
Interview Questions: Tries (ungraded)6m
완료하는 데 8시간 필요

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 videos (Total 75 min), 1 reading, 2 quizzes
5개의 동영상
Brute-Force Substring Search10m
Knuth–Morris–Pratt33m
Boyer–Moore8m
Rabin–Karp16m
1개의 읽기 자료
Lecture Slides10m
1개 연습문제
Interview Questions: Substring Search (ungraded)6m
5
완료하는 데 2시간 필요

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 videos (Total 83 min), 2 readings, 1 quiz
5개의 동영상
REs and NFAs13m
NFA Simulation18m
NFA Construction11m
Regular Expression Applications20m
2개의 읽기 자료
Overview10m
Lecture Slides10m
1개 연습문제
Interview Questions: Regular Expressions (ungraded)6m
완료하는 데 8시간 필요

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 videos (Total 80 min), 1 reading, 2 quizzes
4개의 동영상
Run-Length Coding5m
Huffman Compression24m
LZW Compression27m
1개의 읽기 자료
Lecture Slides10m
1개 연습문제
Interview Questions: Data Compression (ungraded)6m
6
완료하는 데 1시간 필요

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 videos (Total 40 min), 2 readings, 1 quiz
4개의 동영상
Designing Algorithms8m
Establishing Lower Bounds9m
Classifying Problems12m
2개의 읽기 자료
Overview10m
Lecture Slides10m
1개 연습문제
Interview Questions: Reductions (ungraded)6m
완료하는 데 1시간 필요

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 videos (Total 61 min), 1 reading, 1 quiz
4개의 동영상
Simplex Algorithm11m
Simplex Implementations16m
Linear Programming Reductions11m
1개의 읽기 자료
Lecture Slides10m
1개 연습문제
Interview Questions: Linear Programming (ungraded)6m
완료하는 데 2시간 필요

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 videos (Total 85 min), 1 reading, 1 quiz
6개의 동영상
Search Problems10m
P vs. NP16m
Classifying Problems13m
NP-Completeness12m
Coping with Intractability 14m
1개의 읽기 자료
Lecture Slides10m
1개 연습문제
Interview Questions: Intractability (ungraded)6m
5.0
118개의 리뷰Chevron Right

17%

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19%

이 강좌를 통해 확실한 경력상 이점 얻기

10%

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최상위 리뷰

대학: 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.

대학: 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.

강사

Avatar

Robert Sedgewick

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

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer
Computer Science

프린스턴 대학교 정보

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....

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 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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