Algorithmes, Partie I

À propos de ce cours

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.

Résultats de carrière des étudiants

27%

ont commencé une nouvelle carrière après avoir terminé ce cours

29%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

15%

a obtenu une augmentation de salaire ou une promotion

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

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Niveau intermédiaire

Approx. 54 heures pour terminer

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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol

Compétences que vous acquerrez

Data StructureAlgorithmsJava Programming

Résultats de carrière des étudiants

27%

ont commencé une nouvelle carrière après avoir terminé ce cours

29%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours

15%

a obtenu une augmentation de salaire ou une promotion

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 54 heures pour terminer

Anglais

Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol

Enseignants

Évaluation de l'enseignant

4.79/5 (870 notes)

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer

Computer Science

915,572 étudiants

4 cours

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

938,449 étudiants

6 cours

Programme du cours : ce que vous apprendrez dans ce cours

Évaluation du contenu98%(56,068 notes)

Semaine

1

Semaine 1

1 heure pour terminer

Course Introduction

Welcome to Algorithms, Part I.

1 heure pour terminer

1 vidéo (Total 9 min), 2 lectures, 1 quiz

9 heures pour terminer

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 heures pour terminer

5 vidéos (Total 51 min), 2 lectures, 2 quiz

1 heure pour terminer

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 heure pour terminer

6 vidéos (Total 66 min), 1 lecture, 1 quiz

Semaine

2

Semaine 2

9 heures pour terminer

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 heures pour terminer

6 vidéos (Total 61 min), 2 lectures, 2 quiz

1 heure pour terminer

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 heure pour terminer

6 vidéos (Total 63 min), 1 lecture, 1 quiz

Semaine

3

Semaine 3

9 heures pour terminer

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 heures pour terminer

5 vidéos (Total 49 min), 2 lectures, 2 quiz

1 heure pour terminer

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 heure pour terminer

4 vidéos (Total 50 min), 1 lecture, 1 quiz

Semaine

4

Semaine 4

9 heures pour terminer

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 heures pour terminer

4 vidéos (Total 74 min), 2 lectures, 2 quiz

2 heures pour terminer

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 heures pour terminer

6 vidéos (Total 77 min), 1 lecture, 1 quiz

Semaine

5

Semaine 5

2 heures pour terminer

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 heures pour terminer

3 vidéos (Total 63 min), 2 lectures, 1 quiz

9 heures pour terminer

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 heures pour terminer

5 vidéos (Total 66 min), 1 lecture, 1 quiz

Semaine

6

Semaine 6

1 heure pour terminer

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 heure pour terminer

4 vidéos (Total 50 min), 2 lectures, 1 quiz

26 minutes pour terminer

Symbol Table Applications

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

26 minutes pour terminer

4 vidéos (Total 26 min), 1 lecture

Avis

4.9

1732 avis

5 stars
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4 stars
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1 star
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Meilleurs avis pour ALGORITHMES, PARTIE I

par RB31 mai 2020

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.

par RM31 mai 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.

par BJ2 juin 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.

par RP10 juin 2017

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!

Voir tous les avis

Foire Aux Questions

Quand aurai-je accès aux vidéos de cours et aux devoirs ?
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.
Puis-je obtenir des crédits universitaires si je réussis le Cours ?
Ce Cours n'est pas associé à des crédits universitaires, mais certaines universités peuvent décider d'accepter des Certificats de Cours pour des crédits. Vérifiez-le auprès de votre établissement pour en savoir plus. Les Diplômes en ligne et les Certificats Mastertrack™ sur Coursera apportent la possibilité d'obtenir des crédits universitaires.

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.

Algorithmes, Partie I