À propos de ce cours
4.9
4,226 notes
925 avis
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 31 heure pour terminer

Recommandé : 6 weeks of study, 6–10 hours per week....
Langues disponibles

Anglais

Sous-titres : Anglais, Coréen...

Compétences que vous acquerrez

Data StructurePriority QueueAlgorithmsJava Programming
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 31 heure pour terminer

Recommandé : 6 weeks of study, 6–10 hours per week....
Langues disponibles

Anglais

Sous-titres : Anglais, Coréen...

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
10 minutes pour terminer

Course Introduction

Welcome to Algorithms, Part I....
Reading
1 vidéo (Total 9 min), 2 lectures
Video1 vidéo
Reading2 lectures
Welcome to Algorithms, Part I1 min
Lecture Slides min
Heures pour terminer
6 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....
Reading
5 vidéos (Total 51 min), 2 lectures, 2 quiz
Video5 vidéos
Quick Find10 min
Quick Union7 min
Quick-Union Improvements13 min
Union−Find Applications9 min
Reading2 lectures
Overview1 min
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Union–Find (ungraded) min
Heures pour terminer
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....
Reading
6 vidéos (Total 66 min), 1 lecture, 1 quiz
Video6 vidéos
Observations10 min
Mathematical Models12 min
Order-of-Growth Classifications14 min
Theory of Algorithms11 min
Memory8 min
Reading1 lecture
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Analysis of Algorithms (ungraded) min
Semaine
2
Heures pour terminer
6 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....
Reading
6 vidéos (Total 61 min), 2 lectures, 2 quiz
Video6 vidéos
Stacks16 min
Resizing Arrays9 min
Queues4 min
Generics9 min
Iterators7 min
Stack and Queue Applications (optional)13 min
Reading2 lectures
Overview1 min
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Stacks and Queues (ungraded) min
Heures pour terminer
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....
Reading
6 vidéos (Total 63 min), 1 lecture, 1 quiz
Video6 vidéos
Selection Sort6 min
Insertion Sort9 min
Shellsort10 min
Shuffling7 min
Convex Hull13 min
Reading1 lecture
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Elementary Sorts (ungraded) min
Semaine
3
Heures pour terminer
6 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....
Reading
5 vidéos (Total 49 min), 2 lectures, 2 quiz
Video5 vidéos
Mergesort23 min
Bottom-up Mergesort3 min
Sorting Complexity9 min
Comparators6 min
Stability5 min
Reading2 lectures
Overview min
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Mergesort (ungraded) min
Heures pour terminer
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....
Reading
4 vidéos (Total 50 min), 1 lecture, 1 quiz
Video4 vidéos
Quicksort19 min
Selection7 min
Duplicate Keys11 min
System Sorts11 min
Reading1 lecture
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Quicksort (ungraded) min
Semaine
4
Heures pour terminer
6 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. ...
Reading
4 vidéos (Total 74 min), 2 lectures, 2 quiz
Video4 vidéos
Binary Heaps23 min
Heapsort14 min
Event-Driven Simulation (optional)22 min
Reading2 lectures
Overview10 min
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Priority Queues (ungraded) min
Heures pour terminer
1 heure 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....
Reading
6 vidéos (Total 77 min), 1 lecture, 1 quiz
Video6 vidéos
Elementary Implementations9 min
Ordered Operations6 min
Binary Search Trees19 min
Ordered Operations in BSTs10 min
Deletion in BSTs9 min
Reading1 lecture
Lecture Slides min
Quiz1 exercice pour s'entraîner
Interview Questions: Elementary Symbol Tables (ungraded)8 min

Enseignants

Avatar

Kevin Wayne

Senior Lecturer
Computer Science
Avatar

Robert Sedgewick

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

À propos de 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....

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous achetez un Certificat, vous bénéficiez d'un accès à tout le contenu du cours, y compris les devoirs notés. Lorsque vous avez terminé et réussi le cours, votre Certificat électronique est ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

  • 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.

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