About this Course
최근 조회 584,327

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 32시간 필요

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

영어

자막: 영어, 한국어, 러시아어

귀하가 습득할 기술

Data StructureAlgorithmsJava Programming

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 32시간 필요

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

영어

자막: 영어, 한국어, 러시아어

Course을(를) 수강하는 학습자

  • Software Engineers
  • Machine Learning Engineers
  • Technical Leads
  • Data Scientists
  • Data Engineers

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 10분 필요

Course Introduction

1개 동영상 (총 9분), 2 readings
1개의 동영상
2개의 읽기 자료
Welcome to Algorithms, Part I1m
Lecture Slides
완료하는 데 9시간 필요

Union−Find

5개 동영상 (총 51분), 2 readings, 2 quizzes
5개의 동영상
Quick Find10m
Quick Union7m
Quick-Union Improvements13m
Union−Find Applications9m
2개의 읽기 자료
Overview1m
Lecture Slides
1개 연습문제
Interview Questions: Union–Find (ungraded)
완료하는 데 1시간 필요

Analysis of Algorithms

6개 동영상 (총 66분), 1 reading, 1 quiz
6개의 동영상
Observations10m
Mathematical Models12m
Order-of-Growth Classifications14m
Theory of Algorithms11m
Memory8m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Analysis of Algorithms (ungraded)
2
완료하는 데 9시간 필요

Stacks and Queues

6개 동영상 (총 61분), 2 readings, 2 quizzes
6개의 동영상
Resizing Arrays9m
Queues4m
Generics9m
Iterators7m
Stack and Queue Applications (optional)13m
2개의 읽기 자료
Overview1m
Lecture Slides
1개 연습문제
Interview Questions: Stacks and Queues (ungraded)
완료하는 데 1시간 필요

Elementary Sorts

6개 동영상 (총 63분), 1 reading, 1 quiz
6개의 동영상
Selection Sort6m
Insertion Sort9m
Shellsort10m
Shuffling7m
Convex Hull13m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Elementary Sorts (ungraded)
3
완료하는 데 9시간 필요

Mergesort

5개 동영상 (총 49분), 2 readings, 2 quizzes
5개의 동영상
Bottom-up Mergesort3m
Sorting Complexity9m
Comparators6m
Stability5m
2개의 읽기 자료
Overview
Lecture Slides
1개 연습문제
Interview Questions: Mergesort (ungraded)
완료하는 데 1시간 필요

Quicksort

4개 동영상 (총 50분), 1 reading, 1 quiz
4개의 동영상
Selection7m
Duplicate Keys11m
System Sorts11m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Quicksort (ungraded)
4
완료하는 데 9시간 필요

Priority Queues

4개 동영상 (총 74분), 2 readings, 2 quizzes
4개의 동영상
Binary Heaps23m
Heapsort14m
Event-Driven Simulation (optional)22m
2개의 읽기 자료
Overview10m
Lecture Slides
1개 연습문제
Interview Questions: Priority Queues (ungraded)
완료하는 데 1시간 필요

Elementary Symbol Tables

6개 동영상 (총 77분), 1 reading, 1 quiz
6개의 동영상
Elementary Implementations9m
Ordered Operations6m
Binary Search Trees19m
Ordered Operations in BSTs10m
Deletion in BSTs9m
1개의 읽기 자료
Lecture Slides
1개 연습문제
Interview Questions: Elementary Symbol Tables (ungraded)8m
4.9
1129개의 리뷰Chevron Right

32%

이 강좌를 수료한 후 새로운 경력 시작하기

34%

이 강좌를 통해 확실한 경력상 이점 얻기

16%

급여 인상 또는 승진하기

Algorithms, Part I의 최상위 리뷰

대학: RMJun 1st 2017

This is a great class. I learned / re-learned a ton. The assignments were challenge and left a definite feel of accomplishment. The programming environment and automated grading system were excellent.

대학: RPJun 11th 2017

Incredible learning experience. Every programmer in industry should take this course if only to dispel the idea that with the advent of cloud computing exponential algorithms can still ruin your day!

강사

Avatar

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer
Computer Science
Avatar

Robert Sedgewick

William O. Baker *39 Professor of Computer Science
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

  • Once you enroll, you’ll have access to all videos and programming assignments.

  • 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 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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