課程信息
115,092 次近期查看

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

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中級

完成時間大約為34 小時

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

英語(English)

字幕:英語(English), 韓語

您將獲得的技能

GraphsData StructureAlgorithmsData Compression

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為34 小時

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

英語(English)

字幕:英語(English), 韓語

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 10 分鐘

Introduction

1 個視頻 (總計 9 分鐘), 2 個閱讀材料
1 個視頻
2 個閱讀材料
Welcome to Algorithms, Part II1分鐘
Lecture Slides
完成時間為 2 小時

Undirected Graphs

6 個視頻 (總計 98 分鐘), 2 個閱讀材料, 1 個測驗
6 個視頻
Graph API14分鐘
Depth-First Search26分鐘
Breadth-First Search13分鐘
Connected Components18分鐘
Graph Challenges14分鐘
2 個閱讀材料
Overview1分鐘
Lecture Slides
1 個練習
Interview Questions: Undirected Graphs (ungraded)6分鐘
完成時間為 9 小時

Directed Graphs

5 個視頻 (總計 68 分鐘), 1 個閱讀材料, 2 個測驗
5 個視頻
Digraph API4分鐘
Digraph Search20分鐘
Topological Sort 12分鐘
Strong Components20分鐘
1 個閱讀材料
Lecture Slides
1 個練習
Interview Questions: Directed Graphs (ungraded)6分鐘
2
完成時間為 2 小時

Minimum Spanning Trees

6 個視頻 (總計 85 分鐘), 2 個閱讀材料, 1 個測驗
6 個視頻
Greedy Algorithm12分鐘
Edge-Weighted Graph API11分鐘
Kruskal's Algorithm12分鐘
Prim's Algorithm33分鐘
MST Context10分鐘
2 個閱讀材料
Overview1分鐘
Lecture Slides
1 個練習
Interview Questions: Minimum Spanning Trees (ungraded)6分鐘
完成時間為 10 小時

Shortest Paths

5 個視頻 (總計 85 分鐘), 1 個閱讀材料, 2 個測驗
5 個視頻
Shortest Path Properties14分鐘
Dijkstra's Algorithm18分鐘
Edge-Weighted DAGs19分鐘
Negative Weights21分鐘
1 個閱讀材料
Lecture Slides
1 個練習
Interview Questions: Shortest Paths (ungraded)6分鐘
3
完成時間為 7 小時

Maximum Flow and Minimum Cut

6 個視頻 (總計 72 分鐘), 2 個閱讀材料, 2 個測驗
6 個視頻
Ford–Fulkerson Algorithm6分鐘
Maxflow–Mincut Theorem9分鐘
Running Time Analysis8分鐘
Java Implementation14分鐘
Maxflow Applications22分鐘
2 個閱讀材料
Overview
Lecture Slides
1 個練習
Interview Questions: Maximum Flow (ungraded)6分鐘
完成時間為 2 小時

Radix Sorts

6 個視頻 (總計 85 分鐘), 1 個閱讀材料, 1 個測驗
6 個視頻
Key-Indexed Counting12分鐘
LSD Radix Sort15分鐘
MSD Radix Sort13分鐘
3-way Radix Quicksort7分鐘
Suffix Arrays19分鐘
1 個閱讀材料
Lecture Slides
1 個練習
Interview Questions: Radix Sorts (ungraded)6分鐘
4
完成時間為 2 小時

Tries

3 個視頻 (總計 75 分鐘), 2 個閱讀材料, 1 個測驗
3 個視頻
R-way Tries32分鐘
Ternary Search Tries22分鐘
Character-Based Operations20分鐘
2 個閱讀材料
Overview10分鐘
Lecture Slides
1 個練習
Interview Questions: Tries (ungraded)6分鐘
完成時間為 10 小時

Substring Search

5 個視頻 (總計 75 分鐘), 1 個閱讀材料, 2 個測驗
5 個視頻
Brute-Force Substring Search10分鐘
Knuth–Morris–Pratt33分鐘
Boyer–Moore8分鐘
Rabin–Karp16分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Interview Questions: Substring Search (ungraded)6分鐘
5.0
136 個審閱Chevron Right

15%

完成這些課程後已開始新的職業生涯

20%

通過此課程獲得實實在在的工作福利

13%

加薪或升職

來自算法,第二部分的熱門評論

創建者 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.

創建者 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.

講師

Avatar

Robert Sedgewick

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

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer
Computer Science

關於 普林斯顿大学

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....

常見問題

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 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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