À propos de ce cours
4.9
4,728 notes
1,000 avis

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Dates limites flexibles

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Niveau intermédiaire

Approx. 32 heures pour terminer

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

Anglais

Sous-titres : Anglais, Coréen

Compétences que vous acquerrez

Data StructurePriority QueueAlgorithmsJava Programming

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. 32 heures pour terminer

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

Anglais

Sous-titres : Anglais, Coréen

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
10 minutes pour terminer

Course Introduction

Welcome to Algorithms, Part I....
1 vidéos (Total 9 min), 2 lectures
1 vidéos
2 lectures
Welcome to Algorithms, Part I1 min
Lecture Slides
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....
5 vidéos (Total 51 min), 2 lectures, 2 quiz
5 vidéos
Quick Find10 min
Quick Union7 min
Quick-Union Improvements13 min
Union−Find Applications9 min
2 lectures
Overview1 min
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Union–Find (ungraded)
1 heures 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....
6 vidéos (Total 66 min), 1 lecture, 1 quiz
6 vidéos
Observations10 min
Mathematical Models12 min
Order-of-Growth Classifications14 min
Theory of Algorithms11 min
Memory8 min
1 lectures
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Analysis of Algorithms (ungraded)
Semaine
2
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....
6 vidéos (Total 61 min), 2 lectures, 2 quiz
6 vidéos
Stacks16 min
Resizing Arrays9 min
Queues4 min
Generics9 min
Iterators7 min
Stack and Queue Applications (optional)13 min
2 lectures
Overview1 min
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Stacks and Queues (ungraded)
1 heures 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....
6 vidéos (Total 63 min), 1 lecture, 1 quiz
6 vidéos
Selection Sort6 min
Insertion Sort9 min
Shellsort10 min
Shuffling7 min
Convex Hull13 min
1 lectures
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Elementary Sorts (ungraded)
Semaine
3
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....
5 vidéos (Total 49 min), 2 lectures, 2 quiz
5 vidéos
Mergesort23 min
Bottom-up Mergesort3 min
Sorting Complexity9 min
Comparators6 min
Stability5 min
2 lectures
Overview
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Mergesort (ungraded)
1 heures 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....
4 vidéos (Total 50 min), 1 lecture, 1 quiz
4 vidéos
Quicksort19 min
Selection7 min
Duplicate Keys11 min
System Sorts11 min
1 lectures
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Quicksort (ungraded)
Semaine
4
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. ...
4 vidéos (Total 74 min), 2 lectures, 2 quiz
4 vidéos
Binary Heaps23 min
Heapsort14 min
Event-Driven Simulation (optional)22 min
2 lectures
Overview10 min
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Priority Queues (ungraded)
1 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....
6 vidéos (Total 77 min), 1 lecture, 1 quiz
6 vidéos
Elementary Implementations9 min
Ordered Operations6 min
Binary Search Trees19 min
Ordered Operations in BSTs10 min
Deletion in BSTs9 min
1 lectures
Lecture Slides
1 exercices pour s'entraîner
Interview Questions: Elementary Symbol Tables (ungraded)8 min
4.9
1,000 avisChevron Right

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a commencé une nouvelle carrière après avoir terminé ces cours

34%

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Meilleurs avis

par RMJun 1st 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 RPJun 11th 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!

Enseignants

Avatar

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer
Computer Science
Avatar

Robert Sedgewick

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

À propos de Université de Princeton

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.

  • No. All features of this course are available for free.

  • No. As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with 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.

  • 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.