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.
All the features of this course are available for free.  It does not offer a certificate upon completion.

Карьерные результаты учащихся

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28%

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14%

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Средний уровень

Прибл. 54 часа на выполнение

Английский

Субтитры: Арабский, Французский, Португальский (Европа), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Английский, Испанский

Приобретаемые навыки

Data StructureAlgorithmsJava Programming

Карьерные результаты учащихся

26%

начал новую карьеру, пройдя эти курсы

28%

получил значимые преимущества в карьере благодаря этому курсу

14%

стал больше зарабатывать или получил повышение

100% онлайн

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Средний уровень

Прибл. 54 часа на выполнение

Английский

Субтитры: Арабский, Французский, Португальский (Европа), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Английский, Испанский

Преподаватели

Оценка преподавателя

4.8/5 (оценок: 917)

Kevin Wayne

Phillip Y. Goldman '86 Senior Lecturer

Computer Science

940,435 учащихся

5 курсов

Robert Sedgewick

William O. Baker *39 Professor of Computer Science

Computer Science

963,870 учащихся

7 курсов

Программа курса: что вы изучите

Оценка контента97%(56,863 оценки)

Неделя

1

Неделя 1

1 ч. на завершение

Course Introduction

Welcome to Algorithms, Part I.

1 ч. на завершение

1 видео ((всего 9 мин.)), 2 материалов для самостоятельного изучения, 1 тест

9 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.

9 ч. на завершение

5 видео ((всего 51 мин.)), 2 материалов для самостоятельного изучения, 2 тестов

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.

1 ч. на завершение

6 видео ((всего 66 мин.)), 1 материал для самостоятельного изучения, 1 тест

Неделя

2

Неделя 2

9 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.

9 ч. на завершение

6 видео ((всего 61 мин.)), 2 материалов для самостоятельного изучения, 2 тестов

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.

1 ч. на завершение

6 видео ((всего 63 мин.)), 1 материал для самостоятельного изучения, 1 тест

Неделя

3

Неделя 3

9 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.

9 ч. на завершение

5 видео ((всего 49 мин.)), 2 материалов для самостоятельного изучения, 2 тестов

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.

1 ч. на завершение

4 видео ((всего 50 мин.)), 1 материал для самостоятельного изучения, 1 тест

Неделя

4

Неделя 4

9 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.

9 ч. на завершение

4 видео ((всего 74 мин.)), 2 материалов для самостоятельного изучения, 2 тестов

2 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.

2 ч. на завершение

6 видео ((всего 77 мин.)), 1 материал для самостоятельного изучения, 1 тест

Неделя

5

Неделя 5

2 ч. на завершение

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 ч. на завершение

3 видео ((всего 63 мин.)), 2 материалов для самостоятельного изучения, 1 тест

9 ч. на завершение

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 ч. на завершение

5 видео ((всего 66 мин.)), 1 материал для самостоятельного изучения, 1 тест

Неделя

6

Неделя 6

1 ч. на завершение

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 ч. на завершение

4 видео ((всего 50 мин.)), 2 материалов для самостоятельного изучения, 1 тест

26 минут на завершение

Symbol Table Applications

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

26 минут на завершение

4 видео ((всего 26 мин.)), 1 материал для самостоятельного изучения

Рецензии

4.9

Рецензии: 1754

5 stars
90.86%
4 stars
7.51%
3 stars
0.84%
2 stars
0.24%
1 star
0.52%

Лучшие отзывы о курсе АЛГОРИТМЫ, ЧАСТЬ I

от партнера RB31 мая 2020 г.

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.

от партнера RM31 мая 2017 г.

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.

от партнера RP10 июня 2017 г.

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!

от партнера BJ2 июня 2018 г.

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.

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Часто задаваемые вопросы

Когда я получу доступ к лекциям и заданиям?
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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Алгоритмы, часть I