Algorithms, Part I

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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.

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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。

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。

中級レベル

約54時間で修了

英語

字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, イタリア語, ベトナム語, 韓国語, ドイツ語, ロシア語, 英語, スペイン語

習得するスキル

Data StructureAlgorithmsJava Programming

受講生の就業成果

25%

コース終了後に新しいキャリアをスタートした

28%

コースが具体的なキャリアアップにつながった

15%

昇給や昇進につながった

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。

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。

中級レベル

約54時間で修了

英語

字幕：アラビア語, フランス語, ポルトガル語（ヨーロッパ）, イタリア語, ベトナム語, 韓国語, ドイツ語, ロシア語, 英語, スペイン語

講師

講師の評価

4.8/5 (1,017 件の評価)

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer

Computer Science

受講者988,286 名

5 コース

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

受講者1,012,725 名

7 コース

シラバス - 本コースの学習内容

コンテンツの評価97%(57,952 件の評価)

週

1

週 1

1時間で修了

Course Introduction

Welcome to Algorithms, Part I.

1時間で修了

1件のビデオ（合計9分）, 2 学習用教材, 1 個のテスト

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.

9時間で修了

5件のビデオ（合計51分）, 2 学習用教材, 2 個のテスト

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.

1時間で修了

6件のビデオ（合計66分）, 1 学習用教材, 1 個のテスト

週

2

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.

9時間で修了

6件のビデオ（合計61分）, 2 学習用教材, 2 個のテスト

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.

1時間で修了

6件のビデオ（合計63分）, 1 学習用教材, 1 個のテスト

週

3

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.

9時間で修了

5件のビデオ（合計49分）, 2 学習用教材, 2 個のテスト

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.

1時間で修了

4件のビデオ（合計50分）, 1 学習用教材, 1 個のテスト

週

4

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.

9時間で修了

4件のビデオ（合計74分）, 2 学習用教材, 2 個のテスト

2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.

2時間で修了

6件のビデオ（合計77分）, 1 学習用教材, 1 個のテスト

週

5

週 5

2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.

2時間で修了

3件のビデオ（合計63分）, 2 学習用教材, 1 個のテスト

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.

9時間で修了

5件のビデオ（合計66分）, 1 学習用教材, 1 個のテスト

週

6

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.

1時間で修了

4件のビデオ（合計50分）, 2 学習用教材, 1 個のテスト

26分で修了

Symbol Table Applications

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

26分で修了

4件のビデオ（合計26分）, 1 学習用教材

レビュー

4.9

1804件のレビュー

5 stars
90.88%
4 stars
7.49%
3 stars
0.85%
2 stars
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1 star
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ALGORITHMS, PART I からの人気レビュー

RMにより2017年5月31日

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.

RBにより2020年5月31日

The course focuses on Java specific implementations of algorithms, so if you're not from a Java background, you would have a little difficulty with the assignments.The course content however is great.

BJにより2018年6月2日

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.

RPにより2017年6月10日

Incredible learning experience. Every programmer in industry should take this course if only to dispel the idea that with the advent of cloud computing exponential algorithms can still ruin your day!

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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.
コースを修了することで大学の単位は付与されますか？
このコースでは大学の単位は付与されませんが、一部の大学ではコース修了証を単位として承認する場合があります。詳細については、大学にお問い合わせください。Courseraのオンライン学位および Mastertrack™証明書は、大学の単位を取得する機会を提供します。

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

Algorithms, Part I

このコースについて

受講生の就業成果

25%

28%

15%

習得するスキル

受講生の就業成果

25%

28%

15%

講師

Kevin Wayne

Robert Sedgewick

提供：

プリンストン大学（Princeton University）

シラバス - 本コースの学習内容

週 1

Course Introduction

Union−Find

Analysis of Algorithms

週 2

Stacks and Queues

Elementary Sorts

週 3

Mergesort

Quicksort

週 4

Priority Queues

Elementary Symbol Tables

週 5

Balanced Search Trees

Geometric Applications of BSTs

週 6

Hash Tables

Symbol Table Applications

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