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.
算法，第二部分
提供方
算法，第二部分
普林斯顿大学
課程信息
295,133 次近期查看
學生職業成果
18%
完成這些課程後已開始新的職業生涯
22%
通過此課程獲得實實在在的工作福利
16%
加薪或升職
您將獲得的技能
GraphsData StructureAlgorithmsData Compression
學生職業成果
18%
完成這些課程後已開始新的職業生涯
22%
通過此課程獲得實實在在的工作福利
16%
加薪或升職
100% 在線
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中級
完成時間大約為36 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
完成時間為 10 分鐘
Introduction
Welcome to Algorithms, Part II.
完成時間為 10 分鐘
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
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.
完成時間為 2 小時
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
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.
完成時間為 9 小時
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
完成時間為 2 小時
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.
完成時間為 2 小時
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
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.
完成時間為 10 小時
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
完成時間為 7 小時
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.
完成時間為 7 小時
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
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.
完成時間為 2 小時
6 個視頻 （總計 85 分鐘）, 1 個閱讀材料, 1 個測驗
6 個視頻
Strings in Java17分鐘
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
完成時間為 2 小時
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.
完成時間為 2 小時
3 個視頻 （總計 75 分鐘）, 2 個閱讀材料, 1 個測驗
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
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.
完成時間為 10 小時
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
審閱
5.0
來自算法，第二部分的熱門評論
由 IO 提供Jan 20, 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.
由 AK 提供Apr 16, 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.
由 MK 提供Mar 24, 2018
Excellent course that provides a good introduction to more advanced algorithms that build on those presented in part 1 of the course. Thank you Professor Sedgewick and Kevin Wayne.
由 QZ 提供Aug 18, 2018
This class is amazing. If you follow this course schedule strictly, and finish the homework and interview questions, you are gonna to learn a lot of algorithm thinking methods.
由 HK 提供Mar 4, 2020
This was a great overview of more advanced algorithms, and I also got to prep for interviews and use concepts in actual work. The theory at the end was very helpful as well
由 JL 提供Mar 3, 2019
Rich content. Liked the teaching style as well. But wish that it had an as-strong supporting system as Part I's and mentors/fellow students could answer more questions.
由 CC 提供Feb 27, 2018
Prof. Sedgewick does an amazing job in explaining the mechanics of the fundamental algorithms and data structures. I have thoroughly enjoyed this course (and Part I).
由 PC 提供Feb 8, 2018
Awesome course. Helped me understand graph better. The API is simple and effective. I implemented the whole API and have been using it in my other graph solutions
由 LS 提供Mar 4, 2020
I wrote my review on Linkedin. Thank you very much for the great course ! https://www.linkedin.com/pulse/algorithms-part-ii-mission-accomplished-luigi-selmi/
由 HM 提供May 30, 2019
This is an excellent course as an introduction to algorithms. Together with part 1, the Princeton Algorithms series is the best MOOC course I've ever done.
由 TD 提供Mar 19, 2019
Complex concepts in plain language. Challenging and interesting practice exercises. Without them it's impossible to fully understand theoretical part.
由 EP 提供Nov 23, 2019
Excellent course and materials about algorithms and data structures. Very challenging exercises that make you think hard to achieve the passing score.
由 MA 提供Jun 29, 2019
Dr. Sedgewick is simply amazing. I enjoy how he motivates the need for efficient algorithms and applications. I only wish he would offer more courses!
由 IA 提供Mar 3, 2018
Every single programming assignment was both challenging and fun. I learned really good algorithms and overall I feel I'm a better Software Engineer
由 TG 提供Apr 26, 2018
Excellent follow-on from Part I - covers additional algorithms in depth and the excellent examples really help to learn and understand the material.
由 SL 提供Feb 8, 2020
Completed Algorithms I and II. Great course for students to learn data structures is a systematically way! Could be used in my research directly.
由 KR 提供Dec 1, 2017
Provides excellent insights with reference to some of the advanced algorithms. Assignments for a great source of learning in the course.
由 VR 提供Apr 2, 2018
Very well structured course on Algorithms and advanced data structures. Highly advised to anyone pursuing career in programming field.
由 SK 提供Nov 28, 2017
Excellent! Sedgwick explains things in such a way that is both extremely intuitive but also makes the code practically write itself!
由 MR 提供Jan 3, 2018
Very useful! Very interesting! Very good lectures, quite hard assignments, very smart auto-grader. Wide and friendly community!
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
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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.
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