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.
Algorithms, Part I
プリンストン大学（Princeton University）このコースについて
719,681 最近の表示
受講生の就業成果
31%
コース終了後に新しいキャリアをスタートした
31%
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
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53時間で修了
英語
字幕：フランス語, ポルトガル語（ブラジル）, 韓国語, 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
31%
コース終了後に新しいキャリアをスタートした
31%
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
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53時間で修了
英語
字幕：フランス語, ポルトガル語（ブラジル）, 韓国語, ロシア語, 英語, スペイン語
提供：
プリンストン大学（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.
シラバス - 本コースの学習内容
10分で修了
Course Introduction
Welcome to Algorithms, Part I.
10分で修了
1件のビデオ （合計9分）, 2 学習用教材
1件のビデオ
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.
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
5件のビデオ
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.
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
6件のビデオ
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)
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
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.
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
6件のビデオ
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)
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
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.
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
1件の学習用教材
Lecture Slides
1の練習問題
Interview Questions: Quicksort (ungraded)
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
4件のビデオ
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)
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
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)30 分
レビュー
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!
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.
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