このコースについて

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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.

Learner Career Outcomes

32%

コース終了後に新しいキャリアをスタートした

34%

コースが具体的なキャリアアップにつながった

17%

昇給や昇進につながった

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約33時間で修了

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

英語

字幕：英語, 韓国語, ロシア語

習得するスキル

Data StructureAlgorithmsJava Programming

Learner Career Outcomes

32%

コース終了後に新しいキャリアをスタートした

34%

コースが具体的なキャリアアップにつながった

17%

昇給や昇進につながった

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約33時間で修了

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

英語

字幕：英語, 韓国語, ロシア語

シラバス - 本コースの学習内容

週

1

10分で修了

Course Introduction

Welcome to Algorithms, Part I.

1件のビデオ（合計9分）, 2 readings

1件のビデオ

Course Introduction9 分

2件の学習用教材

Welcome to Algorithms, Part I1 分

Lecture Slides

9時間で修了

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.

5件のビデオ（合計51分）, 2 readings, 2 quizzes

5件のビデオ

Dynamic Connectivity10 分

Quick Find10 分

Quick Union7 分

Quick-Union Improvements13 分

Union−Find Applications9 分

2件の学習用教材

Overview1 分

Lecture Slides

1の練習問題

Interview Questions: Union–Find (ungraded)

1時間で修了

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.

6件のビデオ（合計66分）, 1 reading, 1 quiz

6件のビデオ

Analysis of Algorithms Introduction8 分

Observations10 分

Mathematical Models12 分

Order-of-Growth Classifications14 分

Theory of Algorithms11 分

Memory8 分

1件の学習用教材

Lecture Slides

1の練習問題

Interview Questions: Analysis of Algorithms (ungraded)

週

2

9時間で修了

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.

6件のビデオ（合計61分）, 2 readings, 2 quizzes

6件のビデオ

Stacks16 分

Resizing Arrays9 分

Queues4 分

Generics9 分

Iterators7 分

Stack and Queue Applications (optional)13 分

2件の学習用教材

Overview1 分

Lecture Slides

1の練習問題

Interview Questions: Stacks and Queues (ungraded)

1時間で修了

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.

6件のビデオ（合計63分）, 1 reading, 1 quiz

6件のビデオ

Sorting Introduction14 分

Selection Sort6 分

Insertion Sort9 分

Shellsort10 分

Shuffling7 分

Convex Hull13 分

1件の学習用教材

Lecture Slides

1の練習問題

Interview Questions: Elementary Sorts (ungraded)

週

3

9時間で修了

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.

5件のビデオ（合計49分）, 2 readings, 2 quizzes

5件のビデオ

Mergesort23 分

Bottom-up Mergesort3 分

Sorting Complexity9 分

Comparators6 分

Stability5 分

2件の学習用教材

Overview

Lecture Slides

1の練習問題

Interview Questions: Mergesort (ungraded)

1時間で修了

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.

4件のビデオ（合計50分）, 1 reading, 1 quiz

4件のビデオ

Quicksort19 分

Selection7 分

Duplicate Keys11 分

System Sorts11 分

1件の学習用教材

Lecture Slides

1の練習問題

Interview Questions: Quicksort (ungraded)

週

4

9時間で修了

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.

4件のビデオ（合計74分）, 2 readings, 2 quizzes

4件のビデオ

APIs and Elementary Implementations12 分

Binary Heaps23 分

Heapsort14 分

Event-Driven Simulation (optional)22 分

2件の学習用教材

Overview10 分

Lecture Slides

1の練習問題

Interview Questions: Priority Queues (ungraded)

1時間で修了

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.

6件のビデオ（合計77分）, 1 reading, 1 quiz

6件のビデオ

Symbol Table API21 分

Elementary Implementations9 分

Ordered Operations6 分

Binary Search Trees19 分

Ordered Operations in BSTs10 分

Deletion in BSTs9 分

1件の学習用教材

Lecture Slides

1の練習問題

Interview Questions: Elementary Symbol Tables (ungraded)8 分

週

5

1時間で修了

Balanced Search Trees

In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2−3 trees, which are easy to analyze but hard to implement. Next, we consider red−black binary search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems.

3件のビデオ（合計63分）, 2 readings, 1 quiz

3件のビデオ

2−3 Search Trees16 分

Red-Black BSTs35 分

B-Trees (optional)10 分

2件の学習用教材

Overview10 分

Lecture Slides

1の練習問題

Interview Questions: Balanced Search Trees (ungraded)6 分

9時間で修了

Geometric Applications of BSTs

We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kd-trees, a natural generalization of BSTs when the keys are points in the plane (or higher dimensions). We also consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles.

5件のビデオ（合計66分）, 1 reading, 1 quiz

5件のビデオ

1d Range Search8 分

Line Segment Intersection5 分

Kd-Trees29 分

Interval Search Trees13 分

Rectangle Intersection8 分

1件の学習用教材

Lecture Slides

週

6

1時間で修了

Hash Tables

We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption.

4件のビデオ（合計50分）, 2 readings, 1 quiz

4件のビデオ

Hash Tables18 分

Separate Chaining7 分

Linear Probing14 分

Hash Table Context10 分

2件の学習用教材

Overview10 分

Lecture Slides

1の練習問題

Interview Questions: Hash Tables (ungraded)

26分で修了

Symbol Table Applications

We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.

4件のビデオ（合計26分）, 1 reading

4件のビデオ

Symbol Table Applications: Sets (optional)5 分

Symbol Table Applications: Dictionary Clients (optional)5 分

Symbol Table Applications: Indexing Clients (optional)7 分

Symbol Table Applications: Sparse Vectors (optional)7 分

1件の学習用教材

Lecture Slides

講師

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer

Computer Science

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

よくある質問

いつ講座や課題にアクセスできるようになりますか？
Once you enroll, you’ll have access to all videos and programming assignments.
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 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.
Which kinds of assessments are available in this course?
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.
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.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。

Algorithms, Part I