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.
提供方
普林斯顿大学
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 10 分鐘
Introduction
Welcome to Algorithms, Part II.
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.
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 7 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.
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
週
2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.
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 8 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.
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
週
3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.
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.
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
週
4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.
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 8 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.
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
週
5完成時間為 2 小時
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 個視頻 （總計 83 分鐘）, 2 個閱讀材料, 1 個測驗
5 個視頻
REs and NFAs13分鐘
NFA Simulation18分鐘
NFA Construction11分鐘
Regular Expression Applications20分鐘
2 個閱讀材料
Overview10分鐘
Lecture Slides10分鐘
1 個練習
Interview Questions: Regular Expressions (ungraded)6分鐘
完成時間為 8 小時
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 個視頻 （總計 80 分鐘）, 1 個閱讀材料, 2 個測驗
4 個視頻
Run-Length Coding5分鐘
Huffman Compression24分鐘
LZW Compression27分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Interview Questions: Data Compression (ungraded)6分鐘
週
6完成時間為 1 小時
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 個視頻 （總計 40 分鐘）, 2 個閱讀材料, 1 個測驗
4 個視頻
Designing Algorithms8分鐘
Establishing Lower Bounds9分鐘
Classifying Problems12分鐘
2 個閱讀材料
Overview10分鐘
Lecture Slides10分鐘
1 個練習
Interview Questions: Reductions (ungraded)6分鐘
完成時間為 1 小時
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 個視頻 （總計 61 分鐘）, 1 個閱讀材料, 1 個測驗
4 個視頻
Brewer's Problem21分鐘
Simplex Algorithm11分鐘
Simplex Implementations16分鐘
Linear Programming Reductions11分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Interview Questions: Linear Programming (ungraded)6分鐘
完成時間為 2 小時
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 個視頻 （總計 85 分鐘）, 1 個閱讀材料, 1 個測驗
6 個視頻
Search Problems10分鐘
P vs. NP16分鐘
Classifying Problems13分鐘
NP-Completeness12分鐘
Coping with Intractability 14分鐘
1 個閱讀材料
Lecture Slides10分鐘
1 個練習
Interview Questions: Intractability (ungraded)6分鐘
5.0
118 個審閱17%
完成這些課程後已開始新的職業生涯
19%
通過此課程獲得實實在在的工作福利
10%
加薪或升職
熱門審閱
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.
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
Robert Sedgewick
William O. Baker *39 Professor of Computer ScienceComputer Science
Kevin Wayne
Phillip Y. Goldman '86 Senior LecturerComputer 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
我什么时候能够访问课程视频和作业？
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。
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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