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完成時間大約為31 小時

建議:6 weeks of study, 6–10 hours per week....
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Data StructurePriority QueueAlgorithmsJava Programming
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立即開始,按照自己的計劃學習。
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根據您的日程表重置截止日期。
中級

中級

完成時間(小時)

完成時間大約為31 小時

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

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(小時)
完成時間為 10 分鐘

Course Introduction

Welcome to Algorithms, Part I....
Reading
1 個視頻(共 9 分鐘), 2 個閱讀材料
Video1 個視頻
Reading2 個閱讀材料
Welcome to Algorithms, Part I1分鐘
Lecture Slides分鐘
完成時間(小時)
完成時間為 6 小時

Union−Find

We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union−find data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the union−find data type to the percolation problem from physical chemistry....
Reading
5 個視頻(共 51 分鐘), 2 個閱讀材料, 2 個測驗
Video5 個視頻
Quick Find10分鐘
Quick Union7分鐘
Quick-Union Improvements13分鐘
Union−Find Applications9分鐘
Reading2 個閱讀材料
Overview1分鐘
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Union–Find (ungraded)分鐘
完成時間(小時)
完成時間為 1 小時

Analysis of Algorithms

The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs....
Reading
6 個視頻(共 66 分鐘), 1 個閱讀材料, 1 個測驗
Video6 個視頻
Observations10分鐘
Mathematical Models12分鐘
Order-of-Growth Classifications14分鐘
Theory of Algorithms11分鐘
Memory8分鐘
Reading1 個閱讀材料
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Analysis of Algorithms (ungraded)分鐘
2
完成時間(小時)
完成時間為 6 小時

Stacks and Queues

We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems....
Reading
6 個視頻(共 61 分鐘), 2 個閱讀材料, 2 個測驗
Video6 個視頻
Stacks16分鐘
Resizing Arrays9分鐘
Queues4分鐘
Generics9分鐘
Iterators7分鐘
Stack and Queue Applications (optional)13分鐘
Reading2 個閱讀材料
Overview1分鐘
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Stacks and Queues (ungraded)分鐘
完成時間(小時)
完成時間為 1 小時

Elementary Sorts

We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm....
Reading
6 個視頻(共 63 分鐘), 1 個閱讀材料, 1 個測驗
Video6 個視頻
Selection Sort6分鐘
Insertion Sort9分鐘
Shellsort10分鐘
Shuffling7分鐘
Convex Hull13分鐘
Reading1 個閱讀材料
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Elementary Sorts (ungraded)分鐘
3
完成時間(小時)
完成時間為 6 小時

Mergesort

We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottom-up version. We prove that any compare-based sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability....
Reading
5 個視頻(共 49 分鐘), 2 個閱讀材料, 2 個測驗
Video5 個視頻
Mergesort23分鐘
Bottom-up Mergesort3分鐘
Sorting Complexity9分鐘
Comparators6分鐘
Stability5分鐘
Reading2 個閱讀材料
Overview分鐘
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Mergesort (ungraded)分鐘
完成時間(小時)
完成時間為 1 小時

Quicksort

We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3-way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys....
Reading
4 個視頻(共 50 分鐘), 1 個閱讀材料, 1 個測驗
Video4 個視頻
Quicksort19分鐘
Selection7分鐘
Duplicate Keys11分鐘
System Sorts11分鐘
Reading1 個閱讀材料
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Quicksort (ungraded)分鐘
4
完成時間(小時)
完成時間為 6 小時

Priority Queues

We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision. ...
Reading
4 個視頻(共 74 分鐘), 2 個閱讀材料, 2 個測驗
Video4 個視頻
Binary Heaps23分鐘
Heapsort14分鐘
Event-Driven Simulation (optional)22分鐘
Reading2 個閱讀材料
Overview10分鐘
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Priority Queues (ungraded)分鐘
完成時間(小時)
完成時間為 1 小時

Elementary Symbol Tables

We define an API for symbol tables (also known as associative arrays, maps, or dictionaries) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance....
Reading
6 個視頻(共 77 分鐘), 1 個閱讀材料, 1 個測驗
Video6 個視頻
Elementary Implementations9分鐘
Ordered Operations6分鐘
Binary Search Trees19分鐘
Ordered Operations in BSTs10分鐘
Deletion in BSTs9分鐘
Reading1 個閱讀材料
Lecture Slides分鐘
Quiz1 個練習
Interview Questions: Elementary Symbol Tables (ungraded)8分鐘

講師

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Kevin Wayne

Senior Lecturer
Computer Science
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Robert Sedgewick

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

關於 Princeton University

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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  • 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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