课程信息

747,924 次近期查看

Learner Career Outcomes

32%

完成这些课程后已开始新的职业生涯

34%

通过此课程获得实实在在的工作福利

17%

加薪或升职

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为33 小时

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

英语(English)

字幕:英语(English), 韩语, 俄语(Russian)

您将获得的技能

Data StructureAlgorithmsJava Programming

Learner Career Outcomes

32%

完成这些课程后已开始新的职业生涯

34%

通过此课程获得实实在在的工作福利

17%

加薪或升职

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为33 小时

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

英语(English)

字幕:英语(English), 韩语, 俄语(Russian)

讲师

授课教师评分4.82/5 (187 个评分)Info
授课教师 Kevin Wayne 的图片

Kevin Wayne 

Phillip Y. Goldman '86 Senior Lecturer
Computer Science
507,597 个学生
4 门课程
授课教师 Robert Sedgewick 的图片

Robert Sedgewick 

William O. Baker *39 Professor of Computer Science
Computer Science
521,840 个学生
6 门课程

提供方

普林斯顿大学 徽标

普林斯顿大学

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up98%(44,431 个评分)Info
1

1

完成时间为 10 分钟

Course Introduction

完成时间为 10 分钟
1 个视频 (总计 9 分钟), 2 个阅读材料
1 个视频
2 个阅读材料
Welcome to Algorithms, Part I1分钟
Lecture Slides
完成时间为 9 小时

Union−Find

完成时间为 9 小时
5 个视频 (总计 51 分钟), 2 个阅读材料, 2 个测验
5 个视频
Quick Find10分钟
Quick Union7分钟
Quick-Union Improvements13分钟
Union−Find Applications9分钟
2 个阅读材料
Overview1分钟
Lecture Slides
1 个练习
Interview Questions: Union–Find (ungraded)
完成时间为 1 小时

Analysis of Algorithms

完成时间为 1 小时
6 个视频 (总计 66 分钟), 1 个阅读材料, 1 个测验
6 个视频
Observations10分钟
Mathematical Models12分钟
Order-of-Growth Classifications14分钟
Theory of Algorithms11分钟
Memory8分钟
1 个阅读材料
Lecture Slides
1 个练习
Interview Questions: Analysis of Algorithms (ungraded)
2

2

完成时间为 9 小时

Stacks and Queues

完成时间为 9 小时
6 个视频 (总计 61 分钟), 2 个阅读材料, 2 个测验
6 个视频
Stacks16分钟
Resizing Arrays9分钟
Queues4分钟
Generics9分钟
Iterators7分钟
Stack and Queue Applications (optional)13分钟
2 个阅读材料
Overview1分钟
Lecture Slides
1 个练习
Interview Questions: Stacks and Queues (ungraded)
完成时间为 1 小时

Elementary Sorts

完成时间为 1 小时
6 个视频 (总计 63 分钟), 1 个阅读材料, 1 个测验
6 个视频
Selection Sort6分钟
Insertion Sort9分钟
Shellsort10分钟
Shuffling7分钟
Convex Hull13分钟
1 个阅读材料
Lecture Slides
1 个练习
Interview Questions: Elementary Sorts (ungraded)
3

3

完成时间为 9 小时

Mergesort

完成时间为 9 小时
5 个视频 (总计 49 分钟), 2 个阅读材料, 2 个测验
5 个视频
Mergesort23分钟
Bottom-up Mergesort3分钟
Sorting Complexity9分钟
Comparators6分钟
Stability5分钟
2 个阅读材料
Overview
Lecture Slides
1 个练习
Interview Questions: Mergesort (ungraded)
完成时间为 1 小时

Quicksort

完成时间为 1 小时
4 个视频 (总计 50 分钟), 1 个阅读材料, 1 个测验
4 个视频
Quicksort19分钟
Selection7分钟
Duplicate Keys11分钟
System Sorts11分钟
1 个阅读材料
Lecture Slides
1 个练习
Interview Questions: Quicksort (ungraded)
4

4

完成时间为 9 小时

Priority Queues

完成时间为 9 小时
4 个视频 (总计 74 分钟), 2 个阅读材料, 2 个测验
4 个视频
Binary Heaps23分钟
Heapsort14分钟
Event-Driven Simulation (optional)22分钟
2 个阅读材料
Overview10分钟
Lecture Slides
1 个练习
Interview Questions: Priority Queues (ungraded)
完成时间为 1 小时

Elementary Symbol Tables

完成时间为 1 小时
6 个视频 (总计 77 分钟), 1 个阅读材料, 1 个测验
6 个视频
Elementary Implementations9分钟
Ordered Operations6分钟
Binary Search Trees19分钟
Ordered Operations in BSTs10分钟
Deletion in BSTs9分钟
1 个阅读材料
Lecture Slides
1 个练习
Interview Questions: Elementary Symbol Tables (ungraded)8分钟
4.9
1,227 条评论Chevron Right

来自算法,第一部分的热门评论

创建者 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.

创建者 BJJun 3rd 2018

Good contents and the logic of the whole course structure is very clear for a novice like me. The weekly homework is also awesome. Would recommend to anyone who wants to learn about computer science.

常见问题

  • 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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