Об этом курсе
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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....
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Intermediate Level

Промежуточный уровень

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Approx. 31 hours to complete

Предполагаемая нагрузка: 6 weeks of study, 6–10 hours per week....
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English

Субтитры: English, Korean...

Приобретаемые навыки

Data StructurePriority QueueAlgorithmsJava Programming
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Только онлайн-курсы

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Calendar

Гибкие сроки

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Intermediate Level

Промежуточный уровень

Clock

Approx. 31 hours to complete

Предполагаемая нагрузка: 6 weeks of study, 6–10 hours per week....
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English

Субтитры: English, Korean...

Программа курса: что вы изучите

Week
1
Clock
10 минуты на завершение

Course Introduction

Welcome to Algorithms, Part I....
Reading
1 видео (всего 9 мин.), 2 материалов для самостоятельного изучения
Video1 видео
Reading2 материала для самостоятельного изучения
Welcome to Algorithms, Part I1мин
Lecture Slidesмин
Clock
6 ч. на завершение

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 видео (всего 51 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video5 видео
Quick Find10мин
Quick Union7мин
Quick-Union Improvements13мин
Union−Find Applications9мин
Reading2 материала для самостоятельного изучения
Overview1мин
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Union–Find (ungraded)мин
Clock
1 ч. на завершение

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 видео (всего 66 мин.), 1 материал для самостоятельного изучения, 1 тест
Video6 видео
Observations10мин
Mathematical Models12мин
Order-of-Growth Classifications14мин
Theory of Algorithms11мин
Memory8мин
Reading1 материал для самостоятельного изучения
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Analysis of Algorithms (ungraded)мин
Week
2
Clock
6 ч. на завершение

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 видео (всего 61 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video6 видео
Stacks16мин
Resizing Arrays9мин
Queues4мин
Generics9мин
Iterators7мин
Stack and Queue Applications (optional)13мин
Reading2 материала для самостоятельного изучения
Overview1мин
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Stacks and Queues (ungraded)мин
Clock
1 ч. на завершение

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 видео (всего 63 мин.), 1 материал для самостоятельного изучения, 1 тест
Video6 видео
Selection Sort6мин
Insertion Sort9мин
Shellsort10мин
Shuffling7мин
Convex Hull13мин
Reading1 материал для самостоятельного изучения
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Elementary Sorts (ungraded)мин
Week
3
Clock
6 ч. на завершение

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 видео (всего 49 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video5 видео
Mergesort23мин
Bottom-up Mergesort3мин
Sorting Complexity9мин
Comparators6мин
Stability5мин
Reading2 материала для самостоятельного изучения
Overviewмин
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Mergesort (ungraded)мин
Clock
1 ч. на завершение

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 видео (всего 50 мин.), 1 материал для самостоятельного изучения, 1 тест
Video4 видео
Quicksort19мин
Selection7мин
Duplicate Keys11мин
System Sorts11мин
Reading1 материал для самостоятельного изучения
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Quicksort (ungraded)мин
Week
4
Clock
6 ч. на завершение

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 видео (всего 74 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video4 видео
Binary Heaps23мин
Heapsort14мин
Event-Driven Simulation (optional)22мин
Reading2 материала для самостоятельного изучения
Overview10мин
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Priority Queues (ungraded)мин
Clock
1 ч. на завершение

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 видео (всего 77 мин.), 1 материал для самостоятельного изучения, 1 тест
Video6 видео
Elementary Implementations9мин
Ordered Operations6мин
Binary Search Trees19мин
Ordered Operations in BSTs10мин
Deletion in BSTs9мин
Reading1 материал для самостоятельного изучения
Lecture Slidesмин
Quiz1 практическое упражнение
Interview Questions: Elementary Symbol Tables (ungraded)8мин

Преподавателя

Kevin Wayne

Senior Lecturer
Computer Science

Robert Sedgewick

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

О 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....

Часто задаваемые вопросы

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • 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.

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